{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deep GPs and DSPPs w/ Multiple Outputs\n",
    "\n",
    "## Introduction\n",
    "\n",
    "In this example, we will demonstrate how to construct deep GPs that can model vector-valued functions (e.g. multitask/multi-output GPs).\n",
    "\n",
    "This tutorial can also be used to construct multitask [deep sigma point processes](./Deep_Sigma_Point_Processes.ipynb) by replacing `DeepGPLayer`/`DeepGP`/`DeepApproximateMLL` with `DSPPLayer`/`DSPP`/`DeepPredictiveLogLikelihood`.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import torch\n",
    "import tqdm\n",
    "import math\n",
    "import gpytorch\n",
    "from torch.nn import Linear\n",
    "from gpytorch.means import ConstantMean, LinearMean\n",
    "from gpytorch.kernels import MaternKernel, ScaleKernel\n",
    "from gpytorch.variational import VariationalStrategy, CholeskyVariationalDistribution, \\\n",
    "    LMCVariationalStrategy\n",
    "from gpytorch.distributions import MultivariateNormal\n",
    "from gpytorch.models.deep_gps import DeepGPLayer, DeepGP\n",
    "from gpytorch.mlls import DeepApproximateMLL, VariationalELBO\n",
    "from gpytorch.likelihoods import MultitaskGaussianLikelihood\n",
    "from matplotlib import pyplot as plt\n",
    "\n",
    "smoke_test = ('CI' in os.environ)\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Set up training data\n",
    "\n",
    "In the next cell, we set up the training data for this example. We'll be using 100 regularly spaced points on [0,1] which we evaluate the function on and add Gaussian noise to get the training labels.\n",
    "\n",
    "We'll have four functions - all of which are some sort of sinusoid. Our `train_targets` will actually have two dimensions: with the second dimension corresponding to the different tasks."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "train_x = torch.linspace(0, 1, 100)\n",
    "\n",
    "train_y = torch.stack([\n",
    "    torch.sin(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2,\n",
    "    torch.cos(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2,\n",
    "    torch.sin(train_x * (2 * math.pi)) + 2 * torch.cos(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2,\n",
    "    -torch.cos(train_x * (2 * math.pi)) + torch.randn(train_x.size()) * 0.2,\n",
    "], -1)\n",
    "\n",
    "train_x = train_x.unsqueeze(-1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Structure of a multitask deep GP\n",
    "\n",
    "The layers of a multitask deep GP will look identical to the layers of a [single-output deep GP](./Deep_Gaussian_Processes.ipynb)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Here's a simple standard layer\n",
    "\n",
    "class DGPHiddenLayer(DeepGPLayer):\n",
    "    def __init__(self, input_dims, output_dims, num_inducing=128, linear_mean=True):\n",
    "        inducing_points = torch.randn(output_dims, num_inducing, input_dims)\n",
    "        batch_shape = torch.Size([output_dims])\n",
    "\n",
    "        variational_distribution = CholeskyVariationalDistribution(\n",
    "            num_inducing_points=num_inducing,\n",
    "            batch_shape=batch_shape\n",
    "        )\n",
    "        variational_strategy = VariationalStrategy(\n",
    "            self,\n",
    "            inducing_points,\n",
    "            variational_distribution,\n",
    "            learn_inducing_locations=True\n",
    "        )\n",
    "\n",
    "        super().__init__(variational_strategy, input_dims, output_dims)\n",
    "        self.mean_module = ConstantMean() if linear_mean else LinearMean(input_dims)\n",
    "        self.covar_module = ScaleKernel(\n",
    "            MaternKernel(nu=2.5, batch_shape=batch_shape, ard_num_dims=input_dims),\n",
    "            batch_shape=batch_shape, ard_num_dims=None\n",
    "        )\n",
    "\n",
    "    def forward(self, x):\n",
    "        mean_x = self.mean_module(x)\n",
    "        covar_x = self.covar_module(x)\n",
    "        return MultivariateNormal(mean_x, covar_x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The main body of the deep GP will look very similar to the single-output deep GP, with a few changes.\n",
    "\n",
    "**Most importantly** - the last layer will have `output_dims=num_tasks`, rather than `output_dims=None`. As a result, the output of the model will be a `MultitaskMultivariateNormal` rather than a standard `MultivariateNormal` distribution.\n",
    "\n",
    "There are two other small changes, which are noted in the comments."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "num_tasks = train_y.size(-1)\n",
    "num_hidden_dgp_dims = 3\n",
    "\n",
    "\n",
    "class MultitaskDeepGP(DeepGP):\n",
    "    def __init__(self, train_x_shape):\n",
    "        hidden_layer = DGPHiddenLayer(\n",
    "            input_dims=train_x_shape[-1],\n",
    "            output_dims=num_hidden_dgp_dims,\n",
    "            linear_mean=True\n",
    "        )\n",
    "        last_layer = DGPHiddenLayer(\n",
    "            input_dims=hidden_layer.output_dims,\n",
    "            output_dims=num_tasks,\n",
    "            linear_mean=False\n",
    "        )\n",
    "        \n",
    "        super().__init__()\n",
    "        \n",
    "        self.hidden_layer = hidden_layer\n",
    "        self.last_layer = last_layer\n",
    "        \n",
    "        # We're going to use a ultitask likelihood instead of the standard GaussianLikelihood\n",
    "        self.likelihood = MultitaskGaussianLikelihood(num_tasks=num_tasks)\n",
    "    \n",
    "    def forward(self, inputs):\n",
    "        hidden_rep1 = self.hidden_layer(inputs)\n",
    "        output = self.last_layer(hidden_rep1)\n",
    "        return output\n",
    "    \n",
    "    def predict(self, test_x):\n",
    "        with torch.no_grad():\n",
    "\n",
    "            # The output of the model is a multitask MVN, where both the data points\n",
    "            # and the tasks are jointly distributed\n",
    "            # To compute the marginal predictive NLL of each data point,\n",
    "            # we will call `to_data_independent_dist`,\n",
    "            # which removes the data cross-covariance terms from the distribution.\n",
    "            preds = model.likelihood(model(test_x)).to_data_independent_dist()\n",
    "            \n",
    "        return preds.mean.mean(0), preds.variance.mean(0)\n",
    "\n",
    "\n",
    "model = MultitaskDeepGP(train_x.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Training and making predictions\n",
    "\n",
    "This code should look similar to the DGP training code."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "da532814adca48958e1a50c698915ae1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "HBox(children=(HTML(value='Epoch'), FloatProgress(value=0.0, max=200.0), HTML(value='')))"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "model.train()\n",
    "optimizer = torch.optim.Adam(model.parameters(), lr=0.1)\n",
    "mll = DeepApproximateMLL(VariationalELBO(model.likelihood, model, num_data=train_y.size(0)))\n",
    "\n",
    "num_epochs = 1 if smoke_test else 200\n",
    "epochs_iter = tqdm.notebook.tqdm(range(num_epochs), desc=\"Epoch\")\n",
    "for i in epochs_iter:\n",
    "    optimizer.zero_grad()\n",
    "    output = model(train_x)\n",
    "    loss = -mll(output, train_y)\n",
    "    epochs_iter.set_postfix(loss=loss.item())\n",
    "    loss.backward()\n",
    "    optimizer.step()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
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ly3jiiSfIy8vDZDLx5JNP0rVrV4fXvu2227jlllvYtGlT6WuLFi3ikUce4c0338RoNDJ58mR69uzJnDlzmDp1KnPmzGHSpElO76ekpIRevXphNBpRqVTceeedzJgxA4Bp06YxadIkfv31V0aMGFG6A9KjRw9UKhU9e/bknnvucXqc4PJC9DXR1wR1T8yZzFpH7KxfNI9zR/aw/qe53PzErFqd47lxPTAZ9KW/b1n1My2DfsbNzQ2dTlerc9YVwuYImyNoOER/E/1N4PpItrzBhqRfv37ynj17Gvy6Atfh+PHjdO7cubGbIagFjr47SZL2yrLcr5GaJGxOJYi+5rqI7845jW1zoGZ25/N/TtfYwVPeKWNDpXHj/dWHanSu/Kx0Vi14j8MxGzHqdajdtHQfPIobH3qBm4d2o3O4T43OVxniuXVdLqfvrlBvwsut+vvgTdXmXE7fWXNDfHeCqnBmd0SKlkAgEAgEAoGL8crCjfQZMQ61m1W/Qe2mpc/I8bzyw981PpdPYAhaDy9MBj0qjRsmgx6thxde/kGsO5rKwYTcOm69QNA0MVtktp/N4o+DyY3dFIFAIKgVwsEjEAgEAoFA0ASpTF/HkVNGoVDyw1tP1UqPpyA3i0HjpjB9zlIGjZtCQU4mALIM/5xIZ2ds1iXfj0DQlEkv0LF4Vzw7YrMwWxo+w0EgEAjqAuHgEQgEAoFAIGiClNXXcUR5p0zskT2VHl8Z9878nEmPzySybScmPT6Te2d+bvd+zNksNp1MpzFS+wWC+kRnNBNzJpMv1u5l5oO3XJJguUAgEDQ2QmRZIBAIBAKBoAlRXl8nZvViYlYvRqVx45WFG/nh7RlMnPYyRfm5THp8Jm/edbXD45VqDS079eSulz/GJyD4ktu1Pz4XndHM6C5hKBQ1LyksEDQlUvN0HErM5VRaAUazzLqf5pY6SB95+Z3Gbp5AIBDUChHBIxAIBAKBQNCEqExfxxbV89O7z5QuRssfr9K44eUbQLeBI0uP0RVLZCSpOX9Ci9lU+fUdpYbZXtt5NJZVB5Mxmi31dv8CQX0Sm1HIop3nWbwrnqPJ+Tx1XXdmjO5IzOrFyLJMzOrF3DmwFe7u7o3dVIFAIKgxl+zgkSQpWpKkfyVJOi5J0lFJkqbXRcMEAoHAGcLuCASChqShbY4jfZ39m9Ywa/IIYlafRZafIu38u8jyv8SsfpRZU9Ts+3c2Rv0OkA5iMuygMO9vDm5+G1k+RczqBbx0Y3veubc1c55owfv/14qDW7ywZVuVd+g4Sg0r+9q5zCJW7EsUTh6By3EiNZ8/DqaQnn8x4s2RQ3XQtTdw7ty5xmqmmOcIBIJaUxcRPCbgaVmWOwNXAo9KktSlDs4rEDQqkiRx5513lv5uMpkIDg5m3LhxjdgqwQWE3WlGiL4mcAEa3ObkZefTY+gLXDN5O+Gt/8Ddcw+SogDYCXwI9ARkJEmPT4AKT18dEAdyrPVfzgPxwG5gAWEtvyIw/GXG3L2P3MxEFr4RwadPRhN72L3UeTN76lUVIhlmjO7o8LXbB7dn+1nXFF4WNufy5FhyPn8dScVSTkfKcRU5b8LCwhqppUAzmueI/iYQNCyXrMEjy3IKkHLh/wWSJB0HIoFjl3pugaAx8fT05MiRI5SUlODu7s6GDRuIjIxs7GYJEHanuSH6mqCp01A2pyBHye4NPuz9x4e0uNVYLBIHN4NK3ZPw1gZMxr9JOfctSDEgpyApFCDLdBs0mZufmEV+loVVC97jcMxGjHodQOkxMm3JTj3L/k1LMerjaNn5Y84fn8jnT0cD9wEtQd4G7AFKAGskQ6d+Q5GBk3u2YNTrkBRKZIuZ3sOvZ398Lh1CvQnz1dbln6HeETbn8uNIUh4bj6fhTCPcJlh+5fW3sWPtkkYXWm5O8xzR3wSChqVONXgkSWoF9Ma6vVT+vQclSdojSdKejAyhTi9wDa677jrWrFkDwOLFi5kyZUrpe0VFRdx3333079+f3r178/vvvwMQFxfH0KFD6dOnD3369CEmJgaATZs2MXz4cG6++WY6derE7bffLqqR1AHO7I6wOa6F6GsCV6Gu5zpmMxzd6cGClwOYeVtLVn8djFqtwyfoG2558gTPf32Od1ad4anP4wmK+ITB491o17MlYS3b07bHALuS5mUjESTp4hRPlmXSzp8p/RdMnD/+ONAeeAmkaOAdYDOQB+xEoboFk0GPt38QPn6BpQ4j2WIGYM+GlTw5qgMtQvxcsqS0sDmXD4cTK3fuQMUqctPf/bLhGlgFzWF9JfqbQNBw1FkVLUmSvIDlwJOyLOeXf1+W5QXAAoB+/fqJXiioNk8+CQcO1O05e/WCTz6p+rjJkyfz+uuvM27cOA4dOsR9993Hli1bAHjrrbcYOXIk3377Lbm5uQwYMIBrrrmGkJAQNmzYgFar5fTp00yZMoU9e/YAsH//fo4ePUpERASDBw9m27ZtDBkypG5v7jKiMrsjbE7NEX1NIKic+pjrDB+uZ+vWSFTqPOBjeg9Pxd0rhe1rfiHpzGQGXj+L/Kx0fnh7RrWqYZWPRMhKTcTDy4dD2zbYVdqyUgK8A/I7QCAwEK3ntSgUN1BcuIS23b+hIGcpAP1HTaQgN4tT+7ZhMZtRu2npPngUEx58nl3nshnYNrA6t2uHsDmC+uZ0WgF/n6jcudOUqUubI/qbQHB5UCcOHkmS1FiNzyJZllfUxTkFgqZAjx49iIuLY/HixVx//fV2761fv55Vq1bxwQcfAKDT6YiPjyciIoLHHnuMAwcOoFQqOXXqVOlnBgwYQFRUFAC9evUiLi5ODEi1RNid5oXoa4KmTn3ZnJiYOwAZk3EVYGT/pjLvXSh3bkuzWv/TXG5+Ylal57t35uel/5/0+EwAls2ZidloQFIokC32wsiSQkFgeDQ3PzGLw1s3kJ+9itufv5bv3yjmxO4HGPfADYy8Naf0PLLFUkanxAufgGB2x2XTPtSLIC+3OviLNAzC5jR/0gt0rDuaWivnzunDGhIiITq67ttVXZrTPEf0N4Gg4bhkB48kSRLwDXBcluWPLr1JAoE91dkZqE8mTJjAM888w6ZNm8jKuigoKcsyy5cvp2PHjnbHz5o1i9DQUA4ePIjFYkGrvahN4OZ2cfKrVCoxmaqoVStwiLA79YPoawKBY+rD5ri7u6PT6Rxf74LOjQ2bU8bm8FFp3Hh/9aFqX8sW1ZOWEEthThbFhXnkZ6Vb07hkmQ69B5X+XLgi981K4uf3w1j9dTDFBUrG3pfpVKfEbJHZeCyN2/pHY/1TVQ9hcwT1RbHBxKoDyRjNzr07jiLjTAaJv34M5N9f/Ym/C777rqFabE992BzR3wSCy4O60OAZDNwJjJQk6cCFn+ur+pBA4Crcd999vPbaa3Tv3t3u9WuvvZbPPvusNO93//79AOTl5REeHo5CoeDHH3/EbDZXOKfgkhF2pxki+pqgCVPnNic2NpapU6fi4eFR+pqksE7LZIsZlca6gAmKaGlXvrnPyPG88sPfNbqWTV9k2vsLee6r1bTs1JPB46cyY95vdho+ZVGp4Y4XUhk0Lpd/lgTw65wQ7n7FXqekbLRQSp6O/Qm5Nf0zNCrC5jRPzBaZPw4mU6CrfNFvqx63/qe5ACSeduOjx1rwz5IAho0v5NNPG6K1Tml28xzR3wSChqEuqmhtBaq/XSMQuBhRUVFMnz69wuuvvvoqTz75JD169ECWZVq1asXq1auZNm0akyZN4tdff2XEiBF4eno2QqubN8LuNE9EXxM0VerD5oSHh+Pj44NOp0OSFMiyhR5DRpNw6ggA986cy461Szi++79y5Zu9qtThqQpHaVyOUChh0uPpuHuZ+fuXQIrzldz+QipqjeOoiO1ns2gf4oW3Vn1J7WsohM1pnvx9PI3kXMfRcQDPjethp0cVs/pXYla3A17FJ0Di/95MpEfPFMaNe4YlS5Y0Srn05jjPEf1NIGgYpMZQHe/Xr59sE8kSCBxx/PhxOnfu3NjNENQCR9+dJEl7ZVnu10hNEjanEkRfc13Ed+ecxrY5UD27c9NNNxEeHo53r2vZ+oc15ams8wXgu9mP4RMQbJcWVf6Y+qJsCsu+f9qzakEIbXsWc/+sZLSeFoefaRfixfieEU7PKZ5b18UVvrv98TlsOll5NamkM8f44sX70RX7YjbeBTwIRNB9SDq3PpmPp4+F1fPfYNPvP/PQQw8xb968Kq/bVG2OK3xnAseI705QFc7sTp1V0RIIBAKBQCAQVJ8VK6y6qZ//c9ppJE11o23qg7IpLDc/MQsvPzO/fBjG3Gei+L+3kvAJqJgycSa9kNiMQtoEezVoWwWC3GIDW09XTDcsz18/HKYobwFwA1a1inV4eD3FpMeeYPbUq+2ie+bPn8/8+fPRarWUlJTUW9sFAoGgrhAOHoFAIBAIBAJBKRVTWC6KO98/+wwL34jgs6eieeidJIIijBU+/+/JDKIDPFAr60LqUSCoHhuPp2OyOM9MsD7XjwP/AzKBD4EvgXMUF8L6n5S8snAjqxa8x5GYjRj0Ojw8PJg4cWJphSeBQCBo6oiRVyAQCAQCgcDFkCRQKyW0aiVebip83dUEeGoI8NTg76HG78JPkJeGFgEedArzpncLPwa0DsDTTVnpuV9ZuJE+I8Y5FHfuPKCYR95PRFek5KtXIjEaKsqE5JcY2XUuu17uWyBwxJGkPBKyi52+n5+Vjqfv+8D/kBS/AlHAC8C50mNiVi9m1pSh7P9vLUaDHq1Wi06nw8fHp1F0eAQCgaA2iAgegUAgEAgEgiaORqUgzEdLuJ+WCF93wny1aNWVO2qc0b9VAPvic9h7PgeDqaKWjk9gCFoPL6fizi0767jp0eP8+E43Vn+jZeIjFVNX9p7PoVOYN4FebhXeEwjqkiK9iS1VpGZ9MzOFvIwn0HqsQlc8FZVGiclgrVKXl5WGUa9D7aal++BRFBfkERndgg9fe4YFCxaQkpLSQHciEAgEl45w8AgEAoFAIBA0Idw1SkJ93Aj20hLs7Uawtxv+HmokqW6K6mhUCq5sE0iPKF92nsvmSGJehdSWgtwsBo2bYifuXJazh98GRrN15R1ccW0iEW0Mdu+bLTL/nEjnln7RddJmgcAZ/53KQGd0XEL72bE9MBtfBGYCC9EV3wdYkC0Sg8dPdVil7o4XPiDY242ePVsyd+7chrwVgUAguGSEg0cgEAgEAoGgkZEkaBXoSbdIH1oHeaFU1H+FZA+NihEdQxjSLoi0fB0peTqSc0tIydM5FXe21+dZhyxfzwcPp6NUD+d/aw7YnT8xp4TDiXl0j/Kt93sRXJ7EZhRyMrWgwuu2CnADRh9g+5pOSIrvkS33o3bT0H3wKCY8+Dw+AcF8N/sxOvcf5tSRKRAIBK6GcPAIXIKPN5yq0/M9NapDlcekpqby5JNPsnv3btzc3GjVqhWffPIJHTpU/dmybNmyhYcffhi1Ws2aNWuYPn06y5Ytq3Dc8OHD+eCDD+jXr1GrbF72JOWWsDM2i5aBHrQI8CTY+/JKL2iMvgaivwkub65oE0jHMG98tOpGub5aqSDK34Mofw8ALBaZ9cfSOJ6SX+FYmwjt4ZiNGPXZKFXPYDb9wDVTjgCmCsdvPp1BlL87/p4ah9cWNkdQWwwmC/+cSHf43vpF84g93IHYw50IjtpERuL9qDTqCumGjVmlrjEQ82mBoPkjHDwCgQNkWWbixIncfffd/PLLLwAcOHCAtLS0Gg9IixYt4plnnuHee+8FcDgYCZoOZrPM+axizmcVA5l4uamIDvAg0s+dCD8tAZ6aOkuTEFgR/e3yRJZlMgr1pOdbo0EkCRSShFIhoVEq8HBT4qlR4aFRNvs+179VQGM3wQ6FQuLarqFo1Qr2x+favVdRn+cn/EOe5N8lveh/TRwBYfZOHoPJwrqjqdzaLxpFA0QlVQdhc5oH/55Mp0Bn/7xdjDAbAPwHbCIjcTSSAqbPWSqidBoY0dcEgoZHVNESCBzw77//olarefjhh0tf69WrF0OGDOHZZ5+lW7dudO/enSVLlgCwadMmhg8fzs0330ynTp24/fbbkWWZr7/+mqVLl/L6669z++23ExcXR7du3QAoKSlh8uTJ9OjRg9tuu42SkosilevXr2fgwIH06dOHW265hcLCQgBatWrFzJkz6dOnD927d+fEiRMAFBYWcu+999K9e3d69OjB8uXLKz2PoPoU6k0cT8ln4/E0fth+ni83x/L7gSR2xGZxJr2A3GIDsuy8LKugakR/u3zIKzFyJCmPPw+nsGBzLIt2xLPhWBobjqWx/mgafx1JZc2hFH7bn8SiHfEs2BzLnL9Pi4pMjYAkSQzvGMKgtoEV3rPp80yfs5TB46cQEPYORmMJiz/0x5E5TMnTsbMJfYfC5rg+p9IK2HH4DJ8/fQdJZ4/z+dN3kJ+dwSsLN9Jt4N3ASiAZleYO+owcw8yfNxPZthOTHp9pF7XjCEmCNsGeDXEbzR7R1y4dndHMucwiYs5msnJ/Er/simfRzvP8uD2O77ad4/tt51i6O4E/Dibzz4k0dsRmcTqtgLwSY4O1UdC0EBE89URKSgqTJ09myZIlorSiC3LkyBH69u1b4fUVK1Zw4MABDh48SGZmJv379+eqq64CYP/+/Rw9epSIiAgGDx7Mtm3beOCBB9i6dSvjxo3j5ptvJi4urvRc8+fPx8PDg0OHDnHo0CH69OkDQGZmJm+++SYbN27E09OT9957j48++ojXXnsNgKCgIPbt28e8efP44IMP+Prrr3njjTfw9fXl8OHDAOTk5FR5HkHtKDGYic0oIjajqPQ1tVIixFtLlwgfOoZ5o1YK33lNEP2teSLLMsl5OlIuaLqk5uko1FdM4an6PGAyV6z0JGgYrmgTiFat5N+T6aXOm/JpLcs+ncXZQy9w9uBn7PtHR9+rK2qi7DqXTasgjwZqdeUIm+Pa5OuMbDyexvpF8zh3ZA8/vfsM6fFnWf/TXCY8OJtzx14DvFCqrsdkSOLMAUOV57ThrVVxbdcwogOaxrPq6oi+Vn0ssozZImMyy5gtFkoMZhbGxJFTbHDoOC9LTrFjZ46HRkmoj5ZQHy3hvtpLqr4ocB2Eg6eeeOONN9i6dSvPv/waT816n7R8HSazjI+7Cl93NT5aNV5aFWaLjN5oQW8yozdZMFtktGolWrXiwr9KfLSqZh+e7ips3bqVKVOmoFQqCQ0NZdiwYezevRsfHx8GDBhAVFQUYN2diIuLY8iQIU7PtXnzZp544gkAevToQY8ePQDYsWMHx44dY/DgwQAYDAYGDhxY+rmbbroJgL59+7JixQoANm7cWBr6CuDv78/q1asrPY+g7jCaZZJyS0jKLWHz6Qw6h/nQI8pXlAe+RER/c12Sckv472QGafk6h+/bBFAnTnuZ3+a9xV0vf1yqiSFoevSM9kOrVrLuaCrmMtW27AWXFcDtLHqvNb98dBX/W7PZ7hwWWeavI6lc4d90Ix6FzWn6WCwyQb7eGEufO0g7fwaAmNWLiVl9A3ALnQd8xPX3zmbRu0+Tev4M63+ay81PzKr03J3DvRneMUQsgBsA0desWCwyepMZncmCwWS/kWE0W8guqr5z0hHFBmv0z7lM66akJIG/h4YwXy3R/h60DvLEXSOe9+aGcPDUEWaLTGahnhYhfhj0FwedH779ih++/QqlWsP/1hyu1bndNUqi/N2J9vcgOsCDACdChYK6o2vXrg5zeytLxXFzu7iYVyqVmExV71Q7ctzJssyoUaNYvHhxpdcpew1Zliucq6rzCOoHvdHCgYRcDiTkEunnTtdIHzqEiqieyhD9rfmQV2Jk6+lMTqVVjOAoi6Od96oWX4LGpWOYNx4aJasOJpcuROwFl3Wo1NMxGWPoPWw/UPEZyC02ovdu/GgsYXNcl11x2bx84bk7tG1DGQcjID0K8m1EtV/I6QOv8OEjF9+zOn8Wo9K48f7qQ3bnDPLScEWbQDqEejfUbVw2iL5WEVmW0Zss6IzWzf2GRJYhu8hAdpGBY8n5SBJE+LrTOtiTtsFeYo3ZTBArjlogy1ZnzpGkPP4+nsbPO+OZ++8Zft4Zz0vfb6TPiHGo3bQASAqrV7T38Otrfb0Sg5nTaYX8cyKdhTFxfL0llr+PpxGbUSjC1uuJkSNHotfr+eqrr0pf2717N/7+/ixZsgSz2UxGRgabN29mwIABtbrGVVddxaJFiwBrCOuhQ9YJx5VXXsm2bds4c8a6I1VcXMypU5VXPRg9ejSff34xZD4nJ6dW5xHULUm5Jaw/msaCzdY+m+4kmuFyR/Q316fEYGbL6Qx+iImr1Lnz3LgezBjdkZjVi5FlmbTzZ5BlmZjVi5kxuiPPjevRgK0W1JToAA9u6ReFp5t1blNecNls2k1463Xs+TuMDx5516GYrcFkjVpuTITNcU2Sc0vYGZtd+tyZjQYkhW0pEwXyO8A6Ek/fg8mgR5IUpfNxtZuWPiPH88oPfwOgVEh0CPXm5r5R3DmwlXDu1BOir13EaLaQrzOSUagnr8RYI+dOflZ6qc5UXR4vy9a56tbTmSyMiWPpngROpRVgsTTdSEtB1YgInmpiMluIyyrmVFoB5zKLKoTR2bANOka9dSEnW6yTmD0bVrJnw0qHOwc1pUBn4lBiHocS81ArJaIDPOga4UPbYK9mm8pV3bKndYUkSfz22288+eSTvPvuu2i12tKyjoWFhfTs2RNJknj//fcJCwsrFWerCY888gj33nsvPXr0oFevXqUDW3BwMN9//z1TpkxBfyEa7M0336y02sArr7zCo48+Srdu3VAqlcycOZObbrqpxucRVA9bekl100oMJktpn20Z6MHAtoGE+7o3QEtrTkP3NRD9zZXRGc3sO5/D/oRcp+NiWV5xsvOu0rjRY8hoJjz4fH02V1AHhHhrua1fC37bn0hOsbFUcPnK629jx9ol5KR/SVbKFSSffYJ1P3zALU9W1KkoKDGh9lKgkCRhcxA2pzqYLTLrjqZiuRD5YXvu0hJiyU1PIyd9PmaTAngYtZuW7oNHISkU7Pt71YVqbxfLo/eI8uXKNoF4ul1+yyAxn27YvibLMjqjhWKDCdMlOE1sUa/VjXat6fE2jp85z4v3TWLa658xuEc7ekT54qG5/PqJqyM1RvWXfv36yXv27Gnw69YUs0XmfFYRp9IKOZtRWK3JK8B3sx9D6+FFQW4Wp/Ztw2I2lw42Ex58vt50Bnzd1fSM9qNbpA9uKtfOpzx+/DidO3du7GYIaoGj706SpL2yLPdrpCbVyObEZxWzfF9ipccs+3QW29f8wsCxk2udVtIqyIOBbYII89XW6vN1hehrrktjfndGs4U9cTnsT8hBb7QfG6tygC6bM5Pta5eAJCFbLNYdeFku7U+O9Hl6R/vy4YuPVrtwQWPbHHCduU5tKTaY+HlnvF2Z6ouaPBOBFcBTwCd2m1uDAnW0atcBD40Sb626MZouqCWNaXP2xefw30nH0QgH/vPih7cigKdRaeZiNhoYOHYyBTmZ+AQElzof87MzmPvdIoa2r/t5eFO1OWKMbxxkWabEaKZYb8Zcy7V23JlTDBk4wD4N8QIqjRuvLNxYYay110WzP746AQbl57calYLe0X70aekvtKmaIM7sjnDJlcNothCXWcSZ9EJiK4nUqQxbdYllc2YiWyyoNG4Y9TrOHNhR1821I6/EyOZTGeyIzSJUWcIHL07jt2VLCQ8Pr9frCgTNEUeL1OfGdsdkvCh45yinv7rRPXGZxcRlxtMuxIsRnULwugx3EgWuSWahnrWHU8gqdCz+WNXOYdmd98KcLLz8AwmNblMaSu5In+egjxtbt27l9ddfZ968efV5e4Jq4qFRMbJTCL8fSC59bfonv/DFi/ejK16L2bgaeINug/K4+Yn7S4+xWMykJ5wjMDwaN5USjUqoBQgqR2c0szM22+F7RfkKVswNwd3zFL2HpzNw3NJSZ075am8DWgcwuF1QQzVbcBlisVxw7BhM1EWWU3mds7IBA+t/mlthrK3s+Moo7xgqP789kJhLv5YB9Ir2EzbbBRArigvIsszuuBx2ncvCaK6bqKayYcuVqfjXNN2jKgwmCx9/9A67tm9j8sPP8PLbH9I2xIuWAR6ohNCrQFAtHC1Sew27nj0bV6JQKLFY7CPzbP04ICyyRmGxZ9ILScgp5qr2wXSL9K3fmxIILpGjyXn8eyLd4TjpbIJYvshA2UVX+c/PGN2x9PeylXFszJ8/n/nz56PVaikpKbnk+xFcGm2CvWgf6sXptEIAtq9dSlGedSGuVD+D2biP5LOP4O1/0bbpigpRYyY/Kx2NJpJAT02zTS8X1A27zmWjMzrWbfrjq2CK85U8NdeNyLavAlZnTnmuaBPAoLbCuSOoH0xmC8UGMzqjmbrOjTl9cGepzpnJoGf/pjXs++eP0vfLOmNeWbixwvG21MTKqMoxpDda2HYmk/3xOQxuF0TXCB9ht5swdbLalyTpW0mS0iVJOlIX52toCvUmlu9LYtuZzDpz7oB1Ertz3XI+fOQGUstMVMsLSZZdSFZGdQSzygtYbl71M9d2C6dTVBDfbYtjf3yOywgzN0b6oODSaKjvrD5tTtuwacwYfUNpH7L12RmjO7Jn40rAugMNYNTrSgfO2bcPI/bwbvZsWFlBNLaqvqs3WthwLI3lexPJKzbW9S1ViehrrkdDf2dGs4V1R1NZfzTN6Tj5ysJLKzJg+7xK42b3uiQpUCit5/Lw8OD222/n3Llztb2VWuHq85z6ZETHELu5hw2z8SRIs8lO6897D3zPc2O7M2N0R0qKrCLchXnZxJ04zL59+xqr6YIa0FjjRF6JkYMJuQ7fO73fnV3rfBl+Sw6RbSumpdgY2DbQJZ07dWV3xBhffxjNFnKKDWQVGSipQ+eO7Ttbv2geBdkZhLRoy/Q5Sxk0bgptu/fH0zegdKxUadzw8g1g+qdLHR5fkJNZ5fXKC+Y7cwwVG8xsOJbGkt0JonBIE6auwjm+B8bU0bkalLMZhfy04zwJ2cU1ViivDuUnvGVV/Ms7YxwtCJPOHi9tU3UcQZVdr1BvYtPJDJdw9Gi1WrKyssSg5ELIskxWVhZabYNoynxPPdicnBwwm78DjgNJwPcoVffSqd9tdBs8ym7h2qn/VfQbNZHta5YwY3RHZEvF/mTre9V14sZnF/Pjjjj+PZFOTpHj9Je6RvQ116OB+xpn0gv4eWc8x5LznR5ji2CTlEqHRQaqUyHLUWUcSaFAli1YzGa0Wi06nQ4fH59q6fDUMd/jovOc+sbTTcVfMQcczj1e/WkCXn5nSE94mq5XTqXPiHHExsZiMpmQJAkPb1/CW3fE2ITnI4KGtzlliTmT6VCcVl8isXROKIERBkbfkeX0871a+HFlm8D6bGJ98j2XaHfEGF8/yLJMkd5EdpGhWnIeZpOR9IRzmKtR8l2WZc6fOMTOrZtKneZp58/w4SM3sHPdckKi21CUl23njCnMy+bDh2+ocPyOv5ZRlJ9b5do2PyudA1v+ou81NzJ9zlL6XXMjBzb/6fRzKXk6ft4Vz9/H05xG1wkajzpJ0ZJlebMkSa3q4lwNRbHBxPazWRxKzCt9rbaK45VRmUe0OnmVP737DGnnzzBr8pDSczrS/ajO9WzYHD174nLoGe1H90hf3DVNSzgrKiqKxMREMjLqztkmqH+0Wi1RUVH1fp36sjn+/rDiz3gm3/QJ+uIrgbGYTXdzYg+4uadh1K9DodyFxfwf/iFRXHvHI6QnxOITGMLJPVtKF7Y20djKwmidid0ZzTIHEnI5mJhLy0APekX70yrQo95CYUVfc00aoq/FZRYRczaLtGrs0tnGz9yMSPqPmui0yICNnHQVpw94cHq/B8nnrLuQCkkmK/V1PH3exGzOQbak4+ZRgr7kPBp1Lkt/mcyqVXNJSUmpt3t2hivOcxqSkX06EuDv5ySNoCOwn4NbbgRu5NyBbcx+/XXatmmDe2ExBUUlpCglUamlidNQ43tZUvN0nEwrcPjeb/NCyE5RM+1/iWjcHDsvWgZ6MKweBJUbirqwO2KMr3tsOjvmGojsFBfkoS8pIjMzEw9vx+n4FouZorwcPH39yS628P5Hc1Cq3TAbW6JUDcdsGoDJ0JeY1SrgWcCIyWAEjIDlwo8ZsKBQnKdFpyK8A05xeOtXdmtbR9Ig6xfNo6QgD42blsi2nVC7aSkpyKt0TSzLcCgxjzPphQzvGELHMO9q/z0E9UuDjaaSJD0IPAjQokWLhrpsBXRGM3vicjiYeLGka1XCUlC5Tk5VGjrlS4javKGOnDHlF4Q2DYKyVCWY5ex65SnUm9h2JpNd57LoFOZD7xZ+BHq5OTy2oVGr1bRu3bqxmyFwYWprc5YveRN98deEtmzH7c+58/eSHaTFt6Q4vysW8w0YDXcBsONPHYe2JlOU9xRefmkY9UrgX0BPjyGj0bi5c2T737TteUWp86e6YndgHTitQszFBHu7cX33cAI8NbX5U1SK6GuC8qTn69h0MoOk3Ko1bsqPn9mpiWSnJpY6Oa1FBoyYTZ04vqst509oOXvQg4wk67Ps5WuiRScdCgXIgG9QOBaLhL4knKK87hTlqdAXK9DJEm5uMHdu5VFwjU1Tmes0NJIk4WYsYND4KVx5nXXukZWaiIeXz4VNrJeBj3D3eoy2/ZI4r2rBki8WkZWaiEFXwl0vf8y91/SmRaBHY9+KoAmx+XQGtsCTstX1fnxnB+nx73DNlCza9nBspwI8NVzfPRyFonnrhFRlc8QYX7fsj8+5IOlRvSV0datayTL89M4X7N90mlZdbkVXFErq+flAbyAYswmUqgI8fU5RkHsO2aJAkrQo1R6YjEqQFYASkEDSYLEMIe6YzeEyi5jV24lZvQClai/9R7UoDWjYtX6FwzVw+d8r25gsNphZeziFk2kFjBRFQ5oEdVYm/YKHebUsy92qOrYxSofqTWb2nc91WtK1fCRNp35Dyc9K595Zc/EJCK60LLKz98wmSD3vxvnjWhJOaTEZJQLCjASGGwkIMxIQamT5py/gHeBP/9E3sGfjcnLS4/H0dudwzHq7DicpFNaKXGoNZpOxwrXqQqhZkqB9iDdXdw4RpfAEdUp9lA+ta5vj7u6OTlcxUqHsoCbLkJms5r0H3sJi7grYfiIvHJ1OZLsdePkuIzDcxPY1vxAS3Zb0hLMo1ZrS0q21iRDUqBSM6BhClwifGn9WIKguJ1ML2HAstdp6dI7Gz+6DR1GUX4hRfwcG3QSSYz2xmN0B0HqaadOthPa9imnfu5iwVgYUVSSLW8zQyT+Qa3oF4lbNPYjGtjnQ/MukO2L72Sx2xF5Ml1k2Zybb1y5BodJiNq5DqerDyz+k4RdkTVMoO3967NV3mTLg8nGKCSrndFoBqw9djNazPSuBEUPITPodL78sZv5sQelgLalVK5ncPxr/etgUcUZ9lUlv6uurywWDyaqVeMpJRJkznI2REx58Hi+/YOJPaPlsxnJky41AWUdcDnAaOApsA2k73QZG4eMfwPa1S0rnlGXnmCaDntCW7bjjhQ/5b/kPnNiTQ3FhVyym/sBgoI3tboD9wCZgNbATtZs1vVqlccPbP4iCnExMBr1de6uzvnRTKxjaLpjuUaJoSENw2ZZJN5gsHEjIZe/5HKc5go4iadITz5Eef5bZU6+y09Yo68kEyjhh3IhZfZKY1e8hKfrSstO9JJ11w6i3zlw9vM24uVvY9683sqXsbsJSAHatAygrRKm/8KMDjiFb/sU/9DR3vTyVmD++5cDmPxl9x6N2oXWXml4my3AqrYCUvBKu7RpGdIDYSRNcPsTGxvLMM8+w4rff0JWUOIy2kSQIjjTy2k/3XBiwX7owIIbRstPDqN0e48Tu64EJwHpAT1r8KkBGtlgYNG5KrTW+DCaryG1CTjEjOoaIMpWCOkWWZbafzWLnOceliJ3haPzUFw8gP+tZUs65EdlWR0j0JlLjFtF7eAi3v/BQlQ6d8iiU4B8oV9u5I2g8+rfy52RqPjkXxOLLRhT/++tv7N/Un6UfhXL6YCBmY8Vd43vd3Bw62gWXF9lFBtYfSwPKR0AoyEx6A1BRmDuGZ68/WyGyQCFJjO0e3qDOHUHzJrvIwOpDyWQVVl8bsezGe/kx0mzqwj9LurD/Pw8Kst1QKKfjHbCXovwPMJv2ghQLZINcNsV5AT4BwXw3+zG7LI3D2zZUyNqIbNuJqc+9fcHB/jkqjdUZ1GPIXRTktCfuuBaLuT/wFPA8SnU+Rv0qFMq/MBl+Q+PmjtloKG2vQqHkh7eeqjSIoOz9bjRaiM8uZlSXUDFXbSSarYPHaLZwKDGXPXE5FBuqFn+yTUJ2/PkrsixfTI0qF+GkdtPSbdBoBo2bSeIpL7b9EUtmSgTInQG19RiNGUkyMPD6PFp00tGio47AcCOSBCYj5KSryU5Vk5OuwmSUsJglZAtYLBJmE+zZ+BdqN18K84oxlEhYzD3Rl8wkJ03B3GcsePq0ojj/O37/YjmHY+ZVmV5mo7pRPgU6E8v3JdLS3cDHLz7K0qVLGkPQUiBoUMLDw/Hx8UGvr7q0ZPlFrdmYRmiLk9z8RBaJp8+y+INTpJwbBiwHUgltsZVbZ7SmdZeK+ck1jb47lpxPWr6OMV3DCPFpeMFLQfPD5jw8k15Y48+WFWbsOXQaK+f5cHTHAAJCjShUt5F0dmnpsfs3wf5NH1Ua6i1wbVRKBSM7hbJ8XyJgrSZq444XOtGycz6/zQ1hwoOHSTw9o8Ku9l1PvorFIjf7tBqBc/QmM38cTC6VUbDpVR7atgGTYQYwDLgblSaRHkPGV0h5HtohSKT6CeqMM+kFrDuaVi0h5bKU3XgvyM1iwOj78fJ/nB1/enFwcxuUagu+AXuBT+l3jRsqVRHb1y5BpdGUrusczUXL2tRJj89k0uMz7X4vS0XJjmTCWuo5d9R2HTc69JlNekJXCvMmYDLcgVJdRE7aj/QefpoRtw5jx9olHN+9mZy0pEqDCMoHGpxKKyC7SM/4nhH4eQhna0NTJw4eSZIWA8OBIEmSEoGZsix/Uxfnrikms4XDSXnsjsumSF99VW9bhxk19ZEKoXS+gaFkJqehUI3DqJ/Eoa3j2f+vJwBKVQTI25AUa5Ete+k9Iorbn3/E6Q6lSg1u2iQ2/Ox8QXfNlCsqvFZcEMtrtz2PyTCYvMzRwIfs3wTwELAapfo3zMZtlWp81CTKR5bhg3ffZvvWLbz4ymt89/WCSo8XCBqS+rI5aWlp3HHP/YT0H1epfhU417qKau9H6y5rSDl3DwrVOCyme0lLmMhnTypp36uY4Tdn06l/MTbN5NpE32UVGli8K4G+Lf25sk0AKqXYIRHUjmKDid/2J5GeX1EjoLzz0ZkwY3G+J4mnH2T/ppEolXD9vZkMm5RDScHjrFpQ7DD9OT87o9bpxI1BU5rnNHVaBHrQOdyb4ykVUxkGj8/l0FYv1v/Uhq5XtqhQEMKi9eVYSj7dIkV4/+WILMv8dSSV7DJVJC9uqPQAZgO/gPQTZqNcYRMm0s+d3tF+Dd7u+kLYncbDplNavnqko3Gw7Gtv3nV1uY33vVgFke8AtES00VFS+BRm4w9kp1kjZq1ZHICkYPqcpXw3+1EA7p05t8q5aGWUdwYBFSKA8rPX8tqi67GYU4k9kkfMal8ObX2Iff9K7N/0N7JcACReuJeKQQTPje2OyXixv5Y/5udd8YzpGkabYK9a3YOgdtSZBk9NqI8cUYtF5mhyPjvPZVGgq7oEXWVczBn3xGwcgiRNQaGchNnkiUqTj7dfDNfe2YvA8CS+nT2KrlcOZ9hN95R2wrIdyuH5P7XmEfe9+gay05KqvXNvn8cZikJ5I5I0Hov5KmRZDdJ2kD9k4Fg3bpl+0YtbmcDXKws3VjBUzo7XarWUlFQtuikQlKe+ctOrS01sTnxWcenuc235bvZj+AQElw6gWSkWWnf9jO1rfcnNUNOqSwnxpyZhMf1Z4bMKlZpWnXtV2y74e6i5pksoUf5ix1JQM4r01mhNZ2Hn5fXlyv5uFWYMAF7Aqu+pBL5HqX6H/6356+I5LoynpXoBLdqSHn+2xlpUV7QOYFC7oGof39g2By5vPYxig4mFMeftUuNti6Bx989n/vO90XrspvvgTxk49ja7+ZO3VsU9g1oJx/VlSMyZTIdpot/Oepy4Y/MoKfIhMGwMPoEaQqPb2M25lQqJ269o0WjFQoTNcV1SUlKYPHkyS5YsISAohD3ns9l3PsehFp0j3dWy67r0xHP4BIZwYlchJuMM4CYkhZE+I3MYNlFPVHt9BV0eSaFEtpjpN+pGpj77XkPeukPys5Xs/MuXbX94kZ+lBWkfyK+gVP+Du4cnD733HZFtOgHw8/vPs2fjShQKJRaL2aFmjyTBFa0DubJNQL1VhL1ccWZ3moWD50RqPtvPZpF7Ief7UtCXSMx/bjElhSPJz74CfYkayCUk+gATH2lHu97FKC/oD1cmvOwIZ44TSaHgw7+OA1Wna5SdLNvO5R/SCb/gF8lOm0Repidqt1Suv0dB7xEF+ASYKxgSlcYNrbu1g25f/UuFe3AmCHbLoy8xpn8nekT6ivBpQY1o7IlPQzt4nGEywq51vqz/yY/8bDc8fA6hL5mB2fh36QAfHNWKzKTzDu2KM/sgSdAzyo9hHYJF3xRUC2fOnfysdKv2XKVzAxXwOtb8fatjR6X5kB5DOlQQYrQ5O3f8+StmU8UxurrVKoWDx/U4lJjL38fTS38vO2cKjvqE378IYepzKfS7pmKkz1Udgunb0r8BWytobMqLKpdl/7/e/PhOOFOeSaX/6HyHx1zZJpCBbQPrs4mVImyO6zJt2jS+/PJLbr79Hq5+4CWHGSDlI1UcIwFjsI6No4BcYB79RsUz9dkZdkcumzOTmDW/ODxLU0ljNpvhyxc2cubgGKwCzduBVxg4NpTdG1Y4XNMCDBo3xeG6ONLfnTHdwvDRquuz2ZcVzuyOS2+PZBToWbongT8Pp1bp3MnPSufzp+9wGuamL5H4dU4Ir97SlviTr5GR1Al9yQ/AOCCU9IQRfPlSNC/e0IPnxvVgxuiOxKxejCzLxKxezIzRHXluXI9K2/DKwo30GTGuwuuyxVL6+bLpGo4oyM0CSbLrVDnpJzh39G4K84K5bcZxlMoUfv8yhNlT2jBnejRbfo/ixL4su1DowrxsPnz4Bof34Eg0U+vhhZt3AP+eSGfRrnjOZRZVeq8CgaAiKjUMGpdHlyv+D3gcXVEoZuNG4Dtki7U6VkZiXIU+abNfq7/90KF9kGU4kJDL6sMpmMw1yxMXXH4U6k0s2+s4cmf9onnIskxQREvUblaNJ5XGDf/QyAvFBaKQpC3AiwSExwCdUGkex2w85VCz6t6ZnzPp8Zm8+uM/9BkxrvScajctfUaO55Uf/ra7dmXjn8C16B7pS4Sf1uGc6fcvwkHazm/zQ8jPrli1c3dcttPCGILmR4HOWCqqXB6TQWLNt0FEtNHR92rHzp1ALw0DWgfUZxMFzRB3d3ckSWL+/PlYLBaW/vgtDw1r53A912uYtRCOQmG1V2o3LZ36X4WHtx/gCTwCHAfWYq2u+hwKZVsGjz+Kvji2wvkKcrPoP2oinfpfhUJ58Zzlx8W6pKq1cHmUSnD3WglSZ6yRulHA32xfcxcmQzSSpCgd0yWFkk79r6LfqIkU5GQ6PF9STgk/7TjPydSaVSIT1ByXFFnWGc1sj83iUEIelmpGIFWmcxF/0o2f3gknK1XNFWPy6DOigMDw86z5ZiWHY/7GqDfYhZwVZGfwxYv3oy8pqlBCrjJsjhMASVIgy9aFmNpNi8lowGTQE7N6MeBcLPnemZ9XWnJv/U9voC/5hZ5XPUNE6+c4HOPF37+0AmKAg5gMbwPLAPtFYPl7cKYvApBZoGfl/iSi/N0Z2j6YMF8h9CpovtRUBLkyykfxWcxfAy8DzwPXAY9h7Z/WRXXn/ldZIypuH4ZssRB7eDfg3D6cTS/k9wPJjO8ZISoXCBxSoDOyfG9iaZUjG+Wfzczk86X/NxsNaNzcMRlGAj8gyxo69JmDm/saOve70uE4UR5nGwc+AcEVrl1ZsQCB6yBJEiM7hfLqD3/z+5fvVpizDBzrxpcvSKz4PIR7XrNGbpS1t1tOezGqS2gj34WgIdh2JsupiO3WVX5kp6l5+N00FBV9gUgSXNM5FKWIXhXUkNjYWB594knW/LEKQ7n1lI0K8zaL1fFs1OvITkujuOARrBo7/sAuYCoqzWp6DBnOhAdXVZg3lp9TLpszE9liqbK4R11QG83HsuvOQ9u6YzLcB8wEjhActYL0hAcuFBsxEBAaWeV59UYLaw+nEJdVJCrC1iMu5+BJzi1h9aHkSgWUKxe7ujhxfHfVITYt82ftd0H4BJiY9n4ibXvYNGacT0bX/zSXojxrjnBNO2RBbhaDx0+lIDebQ1v+QpIUmAx6+l59AxazyaHTpjyOJsr7N61h3z9/lB5zcPP/OLj5fxd+iwJuAKYBS4BjwBsgLQfZiOpCuldlKu2OSMwpYfGueNqHejGobRABoiSloBlSmwHRGbZqIBf7OXQfvIuSomc5vvMO4FdgJUhPYTae53DMxgqV/Gz0GVmxeghAfHYxK/YlcmPvSLRqB7NhwWVLXGYR64+lOhw/yz+bkkJBYHg0Nz8xi0Ob/2bP372BGQRH5hHdYTZGw+FqjRNlcbZxULFfVG/TRND0CfZ2Y2SfjqxzMJ9q292La+/MYs23wRzcXEDPqwrt7K3v9Fl0DvcW+mLNnLR8HSdSHUfmFOUr2PBzAJ36FdGhT7HDY3pG+RHh516fTRQ0Q2RZJtngRppOidHBWs9GxbFRCbKMLLcjPX4hcAXwG/A+sANJUjgUALdRfk5Z2YZ6XeFsE0Wp1tCyU88qNzBt606zMR+VZh4mw8/A+6Qn3IWb+yiuvSuWzKQ5FORUv+3HkvNJz9cxoWckvh4iZauucSkHT0J2MavKlE50RtnO42ziOGjcTL58MZLT+z3pObSAW55Mw8Pb/rzlO13M6l9KI2xsmAx6JIXCaThaeWwT4u9mP8bg8VPtOrS3X6BDh5IjyrctKzURDy+fCtVKZODkni0Y9XNRab7D3fNeCnIeBhaDfAKfgIXcM3MwezcudmpUqopgOJ1WyNn0IjqHe3Nl20CRWylwaTQqBRqVgifHdKvzqAJnUQxG/TEGjZ3Dyf1XkJXyAMjjkFkBfApsszuHpFCA7HzyAJCSp+PXvYlM7B2Jl5tLmXlBPWAyW9h6JpMDCbkV/IVl7XvZZ9NsNNC+12CKcq/l9MGpGHQarrguj4nT0tG4PVSrdjhzCFUW3SNwfa5sE4CuIMfhImb4LTkc3OLNwjdVwAAgD7hob5/TuFFSUiKiM5ox/53KcLaPwcafA9EVKxj/f47np95aFYPaNZ7ujsA10RnNrDuaSmxGEQU5mZU6WCqOT0ZadJxPwum7kS2FwM2o3dbg4e1H+94T7YrulKU6karV2SipDc7Wwgqlkr0bf6/WBqa9REgacDfwNfqSz1n15QC6DfqSSY+nAdVPrc0sNPDzrnjGdg+nRaBw5NclLiOyfC6ziNUHkzFZKrbXNkGNP3HQoQCWbUFkFSaOIjjqGzKTr0KtkbnxkXSuGJNPdUS9K0uNclQqr6aT0/KVd6pTkass5auVDBw7GWTZ7jX/0Eg69RtGYPh0/lkSQlF+Szx9TQy8Po9B4/PwC6pYgawmYtJKhUT3KF8GtArAUywsBRdobPHBmtoci0Xm0OlzvPDcc/y7bk2F0N1LWXhW1c9z0lX8/YuGnX/5YTZ5AfuwOnp+BCz0vGoMXr4B1bIPHholV3cOpV2IKE95uZJZqOfPI6lkFjgWQyxr3wtyMvEJCOaK625j7XdniDt6I7riNoS31nPdPZl0G1h/2mtV9QshsuzaJOWW8OueBIcL+cQzbnw0rSWhLZeSnXp3hfnV6H6dGlU8V1B/nEkv5I+DyQ7fy0pR8+79reh3TT63zaiozyNJMKlPFNEBTWNhKGyOa5CcW8LawynVrricn5XO+w+Np8sVI+h79UMs+SCC3Mzu+AXvJzdjLCpNdumay9EaybYunDjtZf5d+nWla8jKkCQI9NQQ6mOVxdCZLOiMZvRGM2aLjFatvPCjwE2tJK/YSHJeCXqjNXjBUZGe8lS1geloHdxt4LUERX7Cv7+2QKWWmfBgRum6uuy9/zbvLadrY4UkMbRDEH1aCGH9muLM7rjECvxMegFrD6diduDcgYsRO87SnIoL8vD06YW+ZAZHtkeRmWziqok59L/mNCvmPU6XK6rnjKnOLuOlpHPUNNy9PM7C/Mq/dvMTrwEw/GY9Zw4msOU3f/7+JYB/lgbQa1gBNzyUgbe/uVa6CGaLzIH4XI4l53NN51A6hnnX+D4EgsZGoZDo1bENrSOC2WA0oNVq0RsMtIkIpm/n1qTm6ao9OShPVf3cP8TEzU+YMBtfYec6N+Bx4Hs8fWfQuf9X6EtOVts+FBvM/HEwma4RPgzrGIybSqRsXS4YTBb2ns9hT1y2w40RR/YdQKluR9LZr4k7NoHgKAO3TE+h57ACFHWcJq9RKYjw0xLt70GIt5abV60sfW/6rddgNFvQGS3oTGb0RgvhQuvNpYn0c6dnlB8HEnIrvBfVTk/v4fkc3DIBi9kPlSbHbn61Oy6bjmHeIg28mWG2yGw97TylY823QSiVMmPudhwh379VQJNx7ghcg73ns9l2JsvpetIR6xfNo6Qgj5LCXvz83tXoixXcMj2N47ufp+uVI6pMrbKtC7evWVLtSFWlQsJbq8LXXU2QlxuR/u5E+rnXOO1elmUyCvQk5pawtCSPqyZMpd+YW9m84ntO7d9OcUFujdKiHa2D3b08uO5uA/2uOc/Sj0NZ+nEY+//14dan0vj3V+u9//TuM6THn3W6NrbIMv+dzCCjQM/ITiGolUKX51Jp8hE8Z9ILWXMoxaGYsrOy40BpiHmfkY/g5v4WO//yBUlm0Ng8rr4tG59Ac43LnIPzXUZnbbE5Q9zUClQKqVLtoMYkK0XN1lW+bFvlh7uXhSnPphLROq7KiKXKyM9KZ8WHz7L29xVERoQ3wF0IGgKLRcZgttRooGnsna3a7mrddNNNhIeH8+CDD7JgwQJSUlJYsWIFAMUGE1mFBrKLrD8ZBXqS80qchppDzSL8bLbmiutuY9WCFGIP3YXWU8Pkp1PpNqjmkRS+7mqu7RZGpNAqaNbIsszR5Hy2n82iUG/vhCz7/CHLFex7626PkXTmLcxGJRMeyqD/6HyUSsfPbU2jVX3c1YT6uBHirSXS350wH229pt00ts0BsZteHoPJwo87zpNfUrHq6bkj+Xw2oxch0eu48yVzhSiuSH93bukbhVSdcGuBS7A/PodNJx0vig9t9eL71yMYfUcWg8Yer2Brwny13Novukml7gmb03Qp0pvYeDyN2Izqz53s13VPA+8CZ1GqJvO/tUtq+PmLSAoFM+b+ZmfjfN3VtAn2pFWgJ/4eGry1KhT19GznFhtIztXx/IzHWbP0R7usj+qshSuLtrVYYMdaX5Z9atuQmQL8WeEclQUKBHlpuL57OIFebrW8w8sLZ3anyTt4Np1MZ398rsP3HIWKWXMgB9Lvmof4/QsLqXFXIynUXHldHtdMyUahSGb21KtwdN+Xoq1Rvi0aNy1DR13Pq2+8Q/f2rfD3UCNJEkazhbwSY+lPsd5MscFEscFMscGMwWRGoZCQACTrv7nFBozmhvmeUs5p+PHtcFLPuzFsUjb6ounsXLeoxgYALob+Xz3xdpYv+lZo8zQDZFlm/bE0ekb51ah6WmNPfBpq0pNbbOBIUj7HUvIcOnNr41S2kZ6o5se3w0k6o2XIDTmM/79M1Jqa2QVJsgpSDmoXKKJ5mhkpKSlMvPlW7nz5IwxqX4fHlH/+7EO2JyMpviIgTOb+2cmEtTQ4/Zyz18rTOsiT3i38CPXRNrjgd2PbHBCLLUeczypixb6kCq8v+3QWMatHIEn38vLCeALCLjonbc7Ebxb+xPDeHRqwtYL6Qmc0831MHCUG6zhZ1mFclBfJnOktCG+t59H/JbLyi5l2tkajUjB1QAv8m1hEl7A5TQ+zRWZ/fA47z2VXqd9anvysdH6b9ymHtk1FttyIpPiNHkN+YeK0J6q9yV3ZJnmQtxsdQrxoG+JFUCM4M2wbmLfdeS+fzZ3P+cRkbn/502pXp3ZGflY6C15+m+y0T9EVtcFaKfZdwLrO7jFkdJWBAmqlxPCOIXSLdDyXEVzEpVO0nOEoVKxj37F4+7/Ht6/5YzRK9B+dz6ipiQSGWycLyz6dhyzLBEW0JC8rDaNeh0rjhtbdk4fe+67GbdCqlUT4aQlu04kTf4Zw4EI6h8FgoEN0KMN62U9G1EoFQV5uNerMZotMWr6OxJwSknKLSc7V1dhQVUXZwfXJz438sSCY/5YHoPV8jV7Dwhh521A2r/ieA5v/ZPQdj1Za9q985bKNK37C1/0n3Nzc0Ol0ddpuQcOy6VQGx5Lz6Rnl19hNaZL4eWgY0j6IgW0Dic0o5FBiHgk5xTw79tLLQIdEGZn+SQKrvwli82/+nDngwW1Pp9GyU/X7lCzDgYRczmYUMrxjMO1CRAplc+GxZ19m1/ZtKOd/XMHh4izdFkli0NipGPSz2LOhHZL0H/fP1hLW0r/yz5XB0bPs56FmWIdg2gQL7SeBPS0DPekS4cOxZGvVJPtnbDOyfCdv3rUZlebh0ufJluIwa/br/LPix3rb2RY0HHvickqdO3DxO1777fecPTIXjdbE+ePteG7cRX0em63RuLnxqJhLCqogLrOI/05lkF1UUZu1KmQZYo+04cSez5EtASiUz2Exf4Cnz+RqazA6WqN6enkztGc7ukb4lmrpNBa2aHSAq678BoASg5ljKfkcScqr1d8NrH055dxfBEdORFf0EvAO0Buk+zEbi6pVQMFoltlwLI347GKu7hwiNiRrgUs7eOCi7kzfa6by+/wC9mwci8XsTe/h+Vx7ZxZajySHAsyZyedL/28y6Ck06Nm++pcqd9U9NEpaB3kS4edOhJ97aWQOwAcFOTz88MN26Rx1gVIhlV4PAjBbZBJzionNLOJcRhF5DsKda4ptcF399QdkpyVx18sf07FvK375sA2Htr6Pb1AOkuJ3SgryHOZQVqdy2YQHn+ffE+kMaR8k8itdkJizmRxwEk0nsEepkGgf6k37UG9yiw20/GcP78x8iQNbN1xSGWiVRubGRzLo0LeYXz8J4dMno7liTDKp56dwz6vv4BMQXK3UmQKdiT8OptA2pIARHYPxFtF1Lou7u7ud49yRw8VRmVfZYqbPyEkU5s7n0FZvQltuIO38WLb+fnOpfXdky+2rM9o/yxqVgv6tAujTwg+VsPECJwzrEEx8VjGFelO5ZywJhfJLLObHePDtHhUcjP/9vgilchFarZaSkpJGvAPBpaA3mTmYmAuUd/Ap2LV+kvW/0kgg2W4zVu2mZdDV1/HLN/Mao9kCF0FnNLPxeBqn0worPc7ZXCnumJZVC4KJO+aO1iOW7oPmM/rOIexYm1TjEua2NerYW+/k8MZllORlMbJTaK3uqyFw1yjp29Kfvi39Scgu5khSHqfTC6ulWVTeXqcnHgGmAgeAd9C49abbwPkU5BypdntOphaQlq9jbPdwQhrZIeZquHSKFoDZDDv/9GX9T4HkZ6vockUh192TSWRbqzPHFkZeXoBZUiiQLTJQdaqWm1pBu2AvOoZ5E+3v0eR2j7IK9RxNzudIcl6pWnp1qSxH9MO/jlOQo2TWlM3IlruBdOBFYCEgo9JYo5Ccfd5Wuax8alegl4Yx3cII8Rad1VXYez6HzacuDmxTBrQQKVo15OGHH2bBgq9QqdWYapju6Ij0hCzmPptMQfYkIJYOvRdiMv1JQFgkezf+Xu3za1QKhnUIFqGwLkhesZEf/jnA9x+9XmV1xw8fvYkCu8mpGlgK3IhVX+Aju3PbxsHqVGccPG4yb34wh34t/ZtM9cTGtjnQNOxOU+VsRiGrDlijM+xTBb1RKOPpOtDEpEcPVnAw9ht2LSsWfkFYWFgj34GgtpSdT9insbyGdY75IPBVhc9JksQD//cgC778okHbW12EzWl8Mgr0rD6UTG6x841vm2On/FwpN0PFqgXBHPjPG+8AE9fdncmA0fkoLiF4pEWAB/1bBbh0CfB8nZHd57I5mpxfqaOnspS0pLMt+endcMxGiZseTadj3zP8+E71NfxUCokh7YPoLapsVaBZpmjFHnZn+dwQUmLdaN2tmEmPH+O/FQ/g7f8xz42zTxPas3Fl6f9tAsz9rnFcdWvCg8/jfiFSp12IFy0DPJr0bmSglxtXdQjmyjaBHEnO40B8brWjemy7Z/v+XW33umyxMGN0R1QaN2Yu2sjiD57m1L7bkOXvkKRHad/7V6Y+N8GhUKetcllgWJRDdfmsQgO/7EpgcLtA+rTwF6KJTRiLRWZffA5bzziuYiGoPunp6TzyyMP83//9H+998jmnziVe0vne+78hyBYLMBT4ilP7ZwNKYg9bK2xVNw3MYLKw4Vgap9MLuKZzqIjmcRHiMov480gqJjffalV3LMjOICiyFUERLTi5dzey5RdgAuFtPicw/DAn92gdRpdVVp1x6ITJHP/3N+SiHIZ1qF7YukAA0DbYi87h3hxPKajwjJ0+sIzDW+/k6tuiKzzbksaDEpVILXVVLBc0UWzY0liM+nFYnTtfEhSxnrwsbelmbGB4NDc/MYuMw1vIzEhvtLYLmjbHU/L5+3halZqls28fhmyxEHt4N2CbK+UCP6DSeOAX8hWPvN+N4IjAWrVDIUm0DfGkX8uAGm2ENlV8tGqu7hxKv1YB7IzN4nhKgUOdnsoqTfsEFPPM/PP8/L8wfvkwjMDwOLJSzlS74rTJIrPpZAaJOSWM6hLa4Jp+rohLOnjys5T88XUwe//2wT/EyN2vJtNjSCHLP3u30jQhmwDzsJvuKZ2oevsF2j2MUSGB3DeqN5F+7k0uUqcqNCoFfVr40yvKj9jMIs6kF3Ausxid0XnlLluHBJAkBbJsjQAqvwscGJaKLA9GobwLi/lNTu17j+WfFTD2/kyHHfqOFz4ovYajks5mi8zmU5mcTivk6s6hBHsLtfSmRlJuCf+eSCejwHGlOkHNKJvvvPj7r7FYZI6l5LMjNqtGJdcrRt1tAXoC84DXAC+sERnQZ+T4aqeBxWUW8+OO8wzrEEzXCBHN01SxWGR2xGaxKy67tGKbMydM+WclMymOzKQUYDkwFphG6y65IAc6dRDZqmOAvS3vGObN8I7BeDwwvp7vWNBcGd4xhPjs4grPmK5IwVt3m1m1IBgP74rP9u64bFoHeTZiywW15VR6QYXxLjezBLVmIYERObTuEsPJfaZSe2Q2GujQexB9B17FPU/f1aQ3WwWNg9kis/l0RpUSAo4zFpTAm8ALhLYsJKL1sxz47xP+W1bzCGutWkn3SF96RPs2y6Iyvu5qRncNo3+rADafznBYkczZXATAP8RE3LEg4AmyUt4ADhCz+g5iVnesth7lmfRC0gv0jO4SSnSA60ZFNQQul6K1a50Pv80PxmSQ8Pb/nmkfdOb9/xtR4zQhG7Zyb+NvvZPTm1eSnZlutxBzdSwWmaTcEs5mFHIqrcBhZR/b36AgN5tDW/5CkhSAbPf3KlsWb9uqlcQeHUZexp0YDRL+Ib/TtvtfDJ14PTvWLiErNRGDrqTaoXcKSaJPSz+ubBMotHmaAEV6E1tOZ3I8Jd/pMSJFq+4wmCxsO5PJwcTcSkus2ygfBnsRCfgYmA4sAB6h3zUTSjW1qisMCNA2xItRnUNx14hdkqZEkd7En0dSScguBqouVV7+WVFpgkFegsk4glG3H6A4/3+lEzBnZU/Lo1RIXNUhmF7RfvV2n3VBY9scaNp2p6lwJr2QPw4mV3h9+1pffv0klFufSuXK6yqORZMHRBPu694QTRTUIT/vjOf0uXg7u7Xm20D+/iWQpz4/T3QHvcMyzL8sXUaXCJ/Gbn6lCJvT8OhNZlYfTCH+wphYGRXnTpHAYqxR0Auwzp3sxbur43jwdFMysE0QncK9L6s1zNmMQjadzCC/Bjqwtu/g4NYczMZvgTZEtf+e+1/vjG9g9eeokgRdI3wZ2j7oso/maRYpWltW+vHbvBDa9yrGy+8VDvz3CZt+nexU1LeyNCEb9836nAGtAriyTSCKO8Y0wl3VLwqFRHSAB9EBHgxpF8SR5Hz2xGXb7aDYJvLfzX6MweOnOvx7lZ3s3/rUCwAU5Jxj3Y+B7Fh7AwfzxhMYkcO4Bzrzx1fWkpbVDb2zyDJ74nI4nVbIiE4hYmeukZBlmYOJeWw7k+mwSlvZxSS0aPgGNlM0KgUjOoXQMcybjcfTyCqsvHJB2TBYW9RdYHg0WSkJwJNAEfASwZFdOHv4DnLT46vdF22cTS8kLU/HqC6htBL9sUmQkF3Mn0dS7Jz0ZcXtHX2/ZZ8VpWokJsPXQEtum5HKFWM8gIrRlY4iLm14a1WM7REuFtaCOqNdiFXf8GRqgd3rV4zJY+/f3vzxVTBdrijCJ8B+c2p3XA4Teorn0JVIyC4mLV9nZ7eunvwm/63wp+/V+UR3sG7Ulo/oCvFxo3O4SMsT2FOgM7LyQDKZlUSZ222ClBkP4TpgIQqlJ+16zEWhWoqH1zUOJTsq43LeDGsb7EWLAA92x2WzNy4HUzWEmG3fgcW0BqV6EGbjAhJPP8Cab/K4eXo6GrfqBZ3IMhxJyuNcZiHDO4bQIVTYh/K4TATPuh9VrPuxDZK0Elm+FbD3GFYnWqc83loVY7qFEeV/eYV5mS0yR5Pz2B2XUyPPqzPS4jWs+TaIIzFeQAowC/gWsDqRalIKGqB9qBfDOojKPg1JZqGejcfSSMlzXnq0rGC5qjiT35b/Wm2hy8be2XKVXS2T2cKuuGz2nc+pNI+8/A7n4W0b6D54VOnv2/5oBbwF/AbcA1h3wGvaFyUJekb7MbRdkAiNbyRkWWZHbDa7zmWX5r07E8d39P1+M/NJ8jIeIvHsBLTu6US0fZvHPny0xu1oHeTJtV3DXGYi29g2B1zH7jQ2JQYzP2yPo9hg78RJT1DzwcMt6TqwiLtfsa9KKklw18BWBHhqGrKpghqSkpLC5MmTWbJkCS1atsJYwW79CEzi1Z9S8A8xOYxKvLlvlEukYwib03CkF+hYdSC5yvR227x14NjJjL59Gu89eCN+QfNJOXcLHt7xRLWbxcPvWTet7cXe9fgEBDNj3m8Oo2NFcQp7cooMbDieRlJO1dUNy85ft69ZyplDo0iPv5voDjrueS0ZpTL54mayLNvZA2dRy22CPbmqfTD+l+F44MzuuISD5+OPYNWCEGAZ3Qd/T+yR7ehLijAZ9JVG6zgLMbeVgesZ5YdGdfkuWiwWmdPphRxIyCE51/nCvroc2V7Cko/UFOV1AU6iVD9Bz6FudtVcqotGpeDKNoH0jvZzOS0kV8JktrDrXDZ7zudgtsgOjaezxaRSqcRkqp52TGNPfFxt0qMzmjmanMfBhLxqC6aXJT8rna9fO0bi6fuBBJTq/6PnUPda9UWAAE8Ng9sF0S7Eq8afFdSeAp2Rv46kklhu0lRZtYqy329avIZF74aReEbLFWPyuOHhdLQeNRvzQ320DGwb6HKRlY1tc8D17E5jciQpjw3H0iq8vuHnAP78Poj7ZifRbaC95kO3SF9GdWm6JYcFMG3aNL788kvuvu8BWl5zJ6u+LJMiI10J8nbCWi/juS97APYL8pufmEXrIE9u7B3ZuDdRTYTNaRjiMotYczilNNK8+vPWMOBnYARXjMlj4rR0NNqL42FZx8Oid58m9fwZBo2bUiFYINxXy5huYfh5XH7OhMqQZZnDSXlsOe04C6Ayjmz3ZNF7Yag1Mi07vcuxna9ZK3aCnT0obx/KolRI9Ijy5co2gZdV2la9OngkSRoDzMGqVvW1LMvvVnZ8TQyQSvUCZvO7WMu53o4tKgQuVsOqbjlg4dhxTlq+jgMJuZxKLahWmJ0zfv1kJtvXFgMfAG2Jav8b980K5ad3ZzBx2sv8Nu8th15ZZwR5uzGkXRCtAj1Eta06xmS28OveRFLLRO04Mp62xeTFSmttgZHYyphqtVpKSir32tfHxKcmdsdVJz2yLBObWcTeuByScqveGSnLsjkziVlzHvgBaENEm7XcN8ufn//3VI01eWyE+WoZ1DaQloGutdh3Rc5mFLLhWBolBsci+Y7Kl9v6rMUMm5b789fCQNw8LNz6ZBrdB1cURKyMIC8NA9u6rlOvsW0OuK7daQwsFpkftseRU668sckIHz3akpJCBY+8t4eln0wvtV9KhcS9g1uJaN8miLu7OzpdxY1DSaG4UP0RYDPQAWgHFDo8j5ubFp2uZmNfYyFsTv2SXqBjR2w2sRmFdnqFlc1bL+rtXA38BHgDjwA/Oox4rSo6tk9Lf4a2CxIbz5VQoDPyz4l0hyLMlfHs2JsxG5ditQnvYU0hr3yD09F3qFUrubJNAD2i/FBeBt+TM7tzyV4OSZKUwFysCY1dgCmSJHW51PMCLF4MZvO7+IdsBqZS1rkDWDuhJFGQU3kJZ61ayeB2Qdw3uDX9WwUI544DQn20XNs1jDuubEmIT+0rWhXmZTF4vBePf3yK4KitJJ6exCdP+BN7OJGf3n2mNO+6bA52ZWQW6Fm5P4kftp/nUGIuRnPNvMIC5/x9Ir3UufPcuB7MGN2RmNWLkWWZmNWLmTG6I8+N62FXaQ2igA3AW7i7R3H77bdz7ty5Bm97fdqdpoQkSbQN9uKWflFc1SG4RoNVQW4Wg8e35vGPTxIcuYHk2HF8OC2S2MOGKvtdefKz0vn86Ts4dS6BFfuS+HVPAun5lx71J6hIscHE38fTWHUg2alzBy5Wq5g+ZymDxk0pHQfT4jV8+lQ0q78OpvOAIp798nyNnDsKSWJYx2DuuLKlyzp36oPLxeY0FgqFxJVtK5YlVqnhtqfSyM9S8f3rBXbzBrNFZtuZrIZuqqAaxMbGMnXqVDw8rKlVajctfUaOp2PfIfQfNZHIti9jFbd9FbWbie6DR9Ft8CjUbtrS468ZP4m4uIafXzQVhM2xkl6gY9XBZH7eGc/Z9IvOnerMW416GUnxEbARyAYGoHb7lT4jx/PKD39XuNYrCzfSZ8Q4u+ewz8jxvP7zv4zrEc6wDsHCuVMF3lo1N/SKZHjHYBTV2Ji3zS+f/PQNel71CpLie+BFIAakTgAo1RpUag0qtXV9avteHH2HOqOZTScz+D4mjoMJuZgu03VjXXg6BgBnZFmOlWXZAPwC3FAH52XCBLj/yUI69f0SMF+o7mTF9uXO/Hmz01QsjUrBFa0DuHdwKwa0Fo6d6uDvqWFy/xb0buFXreNtHdMmyHzvzM+Z9PhMWndtT076aOAe8rNaA/tJO9+q1Ag7MsiVkV1k4O/j6Xy95RwxZzLRm5wvfARVczAhl2PJFyuTOBvUbMazIDeLAdc+iJvHViAQSboevT4ZHx+fauvw1DH1ZneaIpIk0belP5MHRBPoVb2w4LJ9MTPlemAsJYVaYBcxq1syY3QXnhvXo0IfdkR5h2xiTgm/7E5gT1w2jZHm2xwxXkiX/G5bHIcS86o83vb9RrbtxKTHZ3LXK5/zz1J/PnykBVnJGu58KZl7XkupIE5bGVq1kom9I+nTwl9ETFbksrI5jUHHUG+CvC9uMNls09xn2iPLn5J6/hpkuY/dvOFEar5dFKqgaRAeHo6Pjw86nQ61xg2TQY/Ww4sH3/qKW558l6yUx4AjKNWLMBn0ePsH4eMXWFoe3WTQ0zYyuLHmF02Fy8rmpKSkMGzYMFJTUwHILTaw+lBFx46NquatGcmeePqcRLY8hbvXIqAfKs3Z0mfRURRzWSFm23Po5+vLw2P60l4I+daI3i38mdg70i5dytF80za/3L5mCZ7eapAfQKG6DWgN8h4UyocxGw2YjAZMxovfi0Kh5Ie3nnI6d80vsUYSfbctjr3ns2ucNubq1IXHIxJIKPN74oXX7JAk6UFJkvZIkrQnI8P5QqIs+fkp7N46ipzMRAaPn0r3IaMvnEtRaQdVKST6tPTn3sGtGNROlFCrKUqFxPCOIdzQKwKPKgQ1K4vEsRrfLJSqwUAmsA54E9AgKaznrcwL6wid0czOc9ksjInjaHKeWFzWguTcEv47Zd8HHQ1qZfvXlGfnkhr3CYaScLoN/JQNm+fy8MMPlw7EjUCVdqc2NqepE+KtZcqAFvSMrp6wn22HyxoSvxboBvwOvIOnz2EeeW9baR9e/fUHFQbeynbIzBaZLacz+W1/EkX66mkxCSoiyzLHkvNZGBPntIKdDWfOuJx0FfOejbJG7VxRxHNfxdF7eCE18dEEeGqYMiCaFoFNX8y0kai3uY7AiiRJDCoTxWOzTb2GXU/PoduAXOAlu3mDLMN/p9Ibq8mCSkhLS+O2O+/jiXJRhrvX+6IrDqPLgLU8+eni0vdsUYkvfbGcBx58iPS0ippMlxmXlc1544032Lp1K6/NnMW/J9P5Yft5TqdVdOzYcDZv9fYPZusqX84dXYAkRXL/7CTa9VzI4PETK0S8OsL2HM74bCkTptyDt1x4WYr31gUtAj2YUmZjsuya0dH8MmbNLyBJPPXZ7fgFjwF2YDHPBxYB1ohiW/ZO7JE9TueuZSnUm9h8KpNvt53jQEIulkuQIXElLlmDR5KkW4BrZVl+4MLvdwIDZFl+3NlnqpsjOm3aNL744ksGjr2Nm5+YVaFyTHkhZUmCzuE+DGwbiI/Iya4TCvUm/juZwak0+xKm1a3iYtOJkPEE+SPg/4AYYCoqTWqNNJQcEeGnZXjHEEJ9tLX6/OVGkd7EzzvjKXSwIHfWv0wGia9ejeDsQQ/umZlMt4FFTBnQgjDf6v/N6zo3vaZ2pznmpZ/PKmLj8fRKK+FVzEMHJAXIk4HPAQ3wHDAfkC+8reDDv447/LwzMV8PjZLRXcNcToi3sUnN0/HvyXSHEQiOhCMdaQ0c3OLF0o9DsZglJj2eRt+rC2rk2AFrhawx3cKa1WZIY9scaJ52pyHQuGkdVFsCeB14GejCoHF97OYN13UPo1OYTwO1UFAdivQmfth+Hp3xYhShxQLvP9AKjbuFpz6Pr2CrVAqJW/pF12h+0VQQNqd2ONNrqk7Vz/Lz1vREd8zGT4k94kGn/kVMfjq1RlGsNtqHejGkXZAQUq4jnGpySQpUGo3T+WVuRjpfvXKOlHNTgdOo1HdgNu8vo+NldzLadOtXtbarl4ZhHUKazWaWM7ujqoNzJwLRZX6PApIv5YTlHwRbSk/Zzj7p8Zl2n2kT7MngdkEEedVeP0ZQES83FWN7hNMzx5dNJzPIKLBOul5ZuNHpwq8sNk94WkIshTn/QyaetPjnkDjIdXefIjv1k0pTQ6oiOVfH4l3x9Iz2Y0i7INSilLNTLBaZNYdTHDp3ADtn6aipj/DD2zPITM5ixdz2nN7vyY3TTtFtYEO1tkrq3O64Gi0DPbnzypbEnM3kQEKuw12usjtckqRAli30HDoaL19IjX+AjISXyM+eC0wCHgDOIVsszBjdESSJWYu3VBrZZaPYYGbl/iRCVSXMf+1xfv116eUeWl8pxQYTW09nciwl3+nuZNmdrl3rV9g51K1j4kokxafIlgeI7qjjzhdTCIqoecW1zuHejO4SJnQFquaytzkNxbZ9R3n48Sft5hce3n606hrL0RiZgPD5FOS8Y/eZraczaRvsJeYATYiNx9PsnDsAx3d6kp6o4Y4XUxw6okd0CnFJ50490extTl6JkZWb9/Hmqy+yc9O6StcTjrDNWw16CXevT4k9HICbu4Vbn0rlijH5Nd7sCPPVclWHYCL93GtzOwInxMbG8vTTT/PbbyvR6UpKv2NJoWDf36uczi/9gkNo3WUuKee+AxZhMm6mVZdvCAhdX6bwywVkmdjDu5k99arSTUpHZBYaWL4vkTbBngxqG0Swd/P0G9SFg2c30F6SpNZAEjAZqyJyrYmNjeWZZ55h5cqVFBcXV9rZg7w0DO8YQnRA8/DENVWi/D24/YoWHEnKZ9vZTKgipceGI32krJRMfnw7jD++6k+/a+Yx6fFM8rNSqlVVyxGyDAfic0nILmZMtzBCvMXkoDzxWcVsPn3RQVcV636aR+zhaD54uD0GXRAwnfT4DGBWfTazJtS53XFFNCoFwzuG0DHMm43H0sgsNFQ4xuZkLRuZZXOQW6veeQEfAoeBF1FpvsYvKISslHjW/zTX4eedMfej99i+bSvTn3uZXxZ+LXRcymEwWTiclMfOc1nojY5TscpHR8asXgzY73Sp1D1Qaf5AV9SCkbdlM+auTFS1CFrtHO7DtV1DxfdUPYTNaSD6d21LYICf3fyi65UjuPmJF1n2aQE71w3jkfdaABedBwU6E3vP53Blm4pCzYKG51hyvsMqOpuW++MXbKTn0IIK7/WM9qVbZPXSjy8TmqXNkWWZ0+mF7DufQ0qejvysLA7ujqlyPeGMU/vd+fWTULJSNPS7Jo8JD2bi5VezqB1JgitaB3JlmwAxHtYD4eHh+Pr6YjDocdNqMeit33FBTmaV80trwZBwug8+xOL/RRF37HEKc/sAu5CkTGTZfi5l26SsKgIsNqOI2IwifNzVtAr0oGWgJy0CPJqNXu8lO3hkWTZJkvQYVoEVJfCtLMtHL+WcZcXZNG5uGB10do1KwZVtAukd7Sd2HhsISZLoHuVL+1Avdp3L5vu86i/8yhIYbuSxjxJY/1Mg//4awOEYL8Jabuf88YOs/2muXZnDmjh9sgoN/LIrgUFtA+nbUoiEAmQW6tlyOoO4zOJq/T2ti8tI4DPgegy6Q8CNwDZiVlMaSTclPbfB7sER9WF3XJlwX3cmD2jBqgPJxGcX271X1slaPvLRWvVOQVbao5zYNQX4FJPhVjKT7wPkUueCSuPGpMdnVvi8jfJOiaU/fsvSH791qRK39UlesZEDibkcTc5z6tix4Sw60rbTpVBNxmRcAJKFh95JpGPf4krP54yuET6M6iKcO9VF2JyGRaXPdzi/GHFLDtvX+rL5N3/GP2Cvo7H3fA5dI3xE2fRGplBvYpMDXaTju4o5e8iD0XeeQ1lu9RHgaU2bEFykudkcs8WqN7f3fDY5xRejTdcvmkdBdgahLdtxxwsf1mg9sfk3P37/MpigCCOPvJ9AaPR5vn+jZpvFHholY7qF0TJQpJjXJ2lpaTz88MM8+OCDfPHFlxw5c77S+amNsse8+hNsWJzJ+h8HoFTHEtbyG5LOPIlNYgCoUQQYWMWYDyXmcSgxD6VColukDwPbBOFehQZtU+eSNXhqQ3VyRG+66SbCw8PpPfpmvv/mazu9nc7h3gxpH4yXW10EIAlqS26xgS2nMzmTXljrczw79ibMxveAscAJ4ElgHUgS/a65gb0bf6+VRk+UvzujuoRetvmzepOZLacyOZqcj+VCH3ek31EWk0Hiz4UaNi0PR7bogddAmoskWZAtZjuj+dCYvo2qwVNTXDEvvTaYzBZWH0rhXGb1y2KDNY/d2z8YjfvD/LesB7KsBp5FpfmWHkMqau6Ux5lWzw0PPU/39i3pHe1Pq2aoz5OSksLkyZNZsmSJw5S0hOxi9ifkEpvhXCjSETbtMqVaU6pTlp+dTXbq4yTHjsXb/zSRbWfy4NuzatXu7pG+XN05pFk7dxrb5sDlY3fqi3VHU+2qPdr44a0wTuz25NVF5zDqUu02LjqHezOmW3gjtFYA1uiM3w8kOxyD3r4nk8zkrgy4dgaTn7ZffE3oFUHbYK+Gama9IGyOYwr1Jo4mWRfQZSUCqqvl6QizGX7/Ipitv/vTfXABU59Lxc1drnKeW55IP3eu6x4mnMKNxJn0AtYdTcNgstRoUz8tXs3yz0I5c9ADL78zhLf+mNP7512oti1fkrYrgJvaWoW7V7Q/yiYeRFKfGjz1wooVKwDYdDLdzqs3pH0Q/VsFNFazBGXw89AwvmcECdnW9J/0/Oql/5TlyTlv8sWLd1FSNASL6X/AX8AykJ9gz4aVgGMNpqpIzCnhx+3n6dvSn/6tAy6rvPy0fB1rD6eQe2GHxFHKR/m/Z+xhd5Z+EkJ6ghuB4TFkpdyCSpOFyWBElqlV2Kyg4VEpFYzvGcGawymcrcLxWnYwLbtDUpj9Inv+Hg/MxWQYj0KxqsrvvLJqFnGZxcRlFhPkpaF3C386hXmjaib90Vb14/XXX2fevHmA1cl2IrWA/Qm5ZFYzJbI85dPislJNGHQrSI71YPCEXG54SEalnlXj80oS9G3pz5B2Qc3auSNoHgzrEMz5rCKK9PbpFiNvzeHAfz7ErPYlJ+21Uq2qm5+YxfGUAtqHeru8s8BV2R2XU8G5Y52DhACxwCfsWvctu9Z9WzoHifJ3F99XM0OWZeKzizmUmEdsRlHpRmNZqqvlWR5dscSPb4dzfJcXw2/OZtz9mbxwQ9Xz3LLYxsLBbYNEFkgj0i7EG38PDX8cTGbZp/PsbHllhLYw8sj7iez7x5vfv2zF6f2fEtbqBiY+auHQ5h8vSdsVQG+0sPlUJocS87iyTSCtgzxdrgiFS82yB7YNFM6dJkJKSgrDhg0jNTWV6AAPpg5owbVdw/DW1sxnuH3tUorysrGYVmEt4fwiF6N5pmF7RMuXUreVC046e9xpebzsjDTuvOl6Pl+9h7MZtY8ychVkWWbv+RyW7E4ode6ArVz9ONRu1oibsiVmiwsULPk4lM+fjsZkUPB/byUS0WYWg8cPZ/qcpQSERREQFlWt0pKCpoFSITGuezgdQr0rPa6siG9Z9LozDBr3E1dPPoRCMYL9m97lwH9VT75tTglnz0pmoYENx9L4dts5dsZmVRDfdCXc3d2RJIn58+djsViYP38+kiThptXy9dZzbDiWVmvnTn5WOkX5uYy6fRqRbTvRbdB7JJ5eRMIpLbc/n8Kkx9IpzndcLr38ecoe461VcVPvKIa2DxbOHYFLoFUrGd6xYtpOVHs9kvQ3a74xEbN6eWmJ3RmjO/LcuB6s2HqYoUOvIjU1tRFaffmSkF3M9rNZFV5/ZeFGQqLnYE2jmGM3B5EkqyNP4PrIskxSbgmbT2Xw3bY4VuxL4kx6oUPnDjjfGKpsQyk9Uc3nM6I5uceTsfedIf7kGArzMiqd55ZHq1YyvmcEQ9sHC+dOEyAq2I97h7SxK5dus+WVIUnQ9+oCXvgmjsETCkiPH8X3s0cRHPUxd71cUf+1NuQWG/nrSCpf/HeWRTvP89+pDGIzCjGaK0+1bwo02Qie8lzROkCI5zUhyu9cS5JElwgfOoR6sS8+l91x2RhMzjuA49BMI/AuSMtBngvMBe4EplUw+rbF6U/vPkN6/FmHHl/bMcu+/gQ8ZtEqyIMrWgcS0QzV8UsMZtYfS3UoauhsEM3LjOKrVyIpzlcy4pZsRt+RhZu7TOf+Fw1j2cHRWX6soOmhUEhc1y0MD42SAwm5du9VFdFVNpqn/+hkfn4vjB/eimDXuiKuvy+TqHaOHRfVyaUGKNKbiTmbxZ4Lehl9Wvrj42Lh0bZCAL/99hslJSVo3LR0u7DzWGK4NMeVzW6t+2E+Hj5z+GdJAEERxfgF30/73o8DwXbOOWc7XWWPefWdDxneMcTldqAEgg6h3pwIKagQkXjHiyH8+HY4StV9mE1f2O3+r/z2M7Zvs4+sE9QvRXoTfx5JcbiYV2vDyErpAyxFpcnAZDCUzuk6h/sQ4iMKY7gqFos1Uud0eiHnMgsrRNtVRXWLOGSnqli/KJA9G3xQay3c/0YSR7fbR+9Vx1kU5qvl+u7h+Lq71pyjOVOTwkqO8PC2MOmxdAaPy+X3L4NZOT+EmD/8GPd/GXS9sqjGldQcIcuQnq8nPV/PvvM5aNVKukf60jPat8mm9zVZDR4bm06mo1IoGNI+qJ5bJagO5UvY29BqtZSUXBRTLdKb2HAszakWiDPNDpuQqCxLwBSU6rmYjb54+e1h6nNhfDOzHWaj851xlcZa7q6yvN4of3cGtA5weUE1o9nCucwiTqYWEJdZhMnivC9/N/sxfAKCSwfR7FQjKXGLkCS4b3YykW1rFm0wZUALocHjIsRlFrHhWFpp7ruzvudIZyc/K52Fbz1L+14/s+X3FpQUKOk9PJ8xd2cRHFnzktyOUEgSrYM96RHpS8tAjyYfXZJVqOdsRhEvPfME//z2s51OzqXkfNs73qKBxcBgJMW3DLh2M7v++gEkCdlS0XFeNgzdma5BeRvd3GlsmwOXt92pSwr1Jn7YHmcnUC7L8NotKoryzSjVvbGYip32j8vt2W9oLBaZ5fsSScxx/Df+91d//vgqmJ5DX+aaqVeULuQffH0udw9q1WQXSDXlcrI5KXklnEgt4FRqAcW13NSojuZKboaKjYsD2PmXL5IkM3BcHjGre2E2xlc4VpIUDBo32c5ZVHbjqVe0H1d1CG7ymiqXI4888ggLFixAo9GgNxgYNmEKE6a9VuPzyDIc3+XJ718Gk5GoIaqdjmumZtNtUCGKCzlLtudu4rSX+W3eW7Wq3mxDIUl0CPWiZ7Qf/h4a1EqpwSUIXE6Dx0aXCB9R9roJUd7T6uHhwcSJE/nggw/sjvN0U3FDrwj2xeey7Uwm5nLOB2dRJeVL5mWn3Ufrrl+y9fdeLHhJRWB4OgW5r2IyfovFdHFHT6Vxo8eQ0VaPryxXmtebmFNCYk4SoT5aBra15la6EgnZxRxNzuNsRlGlUVJlKTvI3fjITL54IYrCXAWhLe/B2/9hoPrGLdLPnUCvy1O82hVpFeTJHVe25O8TaZxOK6xRWPT6RfOIO7qT8FYv88rC1/n3V382r/Dn4BZvBl6fx9j7M9B6XNomgUWW2X88lqfunMGjb3zOkB7t6BLh02RE9PUmM6l5OuKzizmbXlha/SM9Lb1WVQSd8crCjaz84kMObWuNxfQKoALpdmTLz+z888JB5TZkHO102XQNjsRsxKDXObXRAoGr4OWm4qr2wWw4llb6WkF2Oibj58AvDL1hB0b9O2SlJuLh5WM39k+cOJE5H3/UeI2/DIg5m+XUuVNcoGDTcn/a9Szm7lfvBi5GePZp6d9snDvNFbNFJqfYQG6xgewiI9lFBlLySuxkAGpLZZGoafFZfPHiKYpy70CWFVwxJo9rpmTjF2xiwKg5fPHi/ehLijAZ9A43qUZNfYQf3p5BfnYG/kEhXN05hK4RvpfcZkH9ULbC1oIFC0hKTqZbpC9HkvJqdB5Jgi5XFNGxbxF7//Zh4+IAvn89grBWekZNyabnVQXVygCpLhZZ5kRqASdSC+zaoFYq8NQoaRnoSasgT6L93UsdP1UV56grmsYMuhKEc6dpUbaEvVarRafT4ePj4/AhlSSJvi39ifJ3txP9teEoNNNxmkc2w2/O5sB/3vz+hR5DyRzgMZCeAnkNkkKB2WiwW6SWX8AqFEp+eOspO09tWr6OlfuTaBHgwdAOQU36WbMJtx5IyCWjltoeNv74Kpizhzxo12seZw8uYv1P5mobtyBvNyb0irisRKubA+4aJeN6RHA8JZ9/TqRXGRZdWRrX9Dn/8dXL54lZfQs71xVwz2vZdBlwaWmPtgF3+defID0xi5izmYT6aGkb7EW7EC8CPBvGoSjLMrnFRlLzdaTklZCcqyOzUO+wAlZ1U9Iqw7aTdOdLH3P+eBtO7JmHxRSOJP2LLD9Iv6u7YTGPs1uw+gaGkpUSj1Ktceic8wkMISIkgANGQ5U2WiBwFbpF+nIitYCE7GLAajP0JUvxC36RHX9246XvXsfLz8yyOTPtxv5ck4rQ0NBGbn3z5VxmEXvOZzt9f+X8EIpylYx/3X6M8XJT0a+l0NRsapgtMsm5JSTmlJCQU0xqnq7CBm11qCw6p7L5xdsrDrNttS9rvgnHZLyCwLD/8PD9hNF3PFd6Hpt2JzgvAGKbU/zz8zx+Xvg1kc1QmqE5YSusBDB37kVdyLbBnmw8nlbj1D+lCgZcm0/fa/I58J83GxcH8OM74fz4ThqQDMiknT8D1LyQT2XPdtn3DAHB5BTnciAhF7VSIjrAAy83FZ/MfpEtW7bwwPTnefSVdzFaZMwWC0azTGZ6Kj+/8zTLfl16yXO2Jp+iJWh62ErY2zytKSkpdp3TEXqTmX+Op9t5OWuC/YBwLfAJ0An4g5adFxLVzq3UQZSflc77D42nyxUjGHbTPexYu4TjuzeTk5bkNI1CkqBTmA+D2gU2GT0Qk9lCSp6OuKwijibn10rbo7wh2rPRm5/fDwfmYC1Jf5GqjJuvu5rb+kfjWYvIisYOXRY25yK5xQbWHE6ptOpdZWlc63+ay/Y1v+AXchs5ae8iSZFcd08uI2/NRqGseJ7KQrCrWyY1wFNDiLcbvh5q/Nw1+Hqo8fdQ46Gp/R6FxSKTV2Ikq8iaV52aryMtX1/n4s+V/Q2WfTqLmNVn8Q5YSEF2J9zc42jTfSnX3dOJnX9aHW/efoF2JdP9QyPp3P8qh45xtVKiR4DMTdcMYty4cTz11FPVttHNjca2OSDsTl2Tnq8jMtivnM3oBBxGUnzHh38Nq5COnJ+dwfc/L6FPC//GanazpUhv4qcd552m6Bza6sX3r0cw+o4sxtxlL748slMIPaP9GqCVDYer2hxZlknILuFQUi5xmUUYzZe+LqysXLmj+UXHfrfRosOrrP1OAbQG1gHPAwcBkBQKlCq1w/mCpFDQbeDV3Dvzc5Gi3Awp0ptYezjFaZSgI8rPu3Iz0pn77B/kZ83AqA8FlgHPAnF2GSDVSdWq7Nmu7L3qzHeXfTqLHWuX8NBDD1VbP86Z3REOHkGDcjgxj00n0yvViylPflY6385+DJ/AEE7u2YJRr0Ol8Sa0xSdkJN6N2Swx8pYcRk3NRqWR7TrYrvUrqrWAtKFUSLQM9KBdiBdtg73qXJS0qtC8vGIjx1PzScwpISW3pEZ/J0eU/Vtcef07fPZUNBGtCwgIvZsjO9ZVqcFiw9NNyW39WuDrUTvnV2NPfITNscdskdl8OoMD8blOj1k2Z6adY8GxvoUvMB+YAtJmXvkhioBQ08VzfFpxsCs78FaWTlmdgdZdoyTAQ0OAp4YALw0eGiWybM1kkpGRZatWldEsYzRbMJgt6AxmsooM5BQZLrl/2ajKiVP+b/Dc2O6YjKHAO8DtQBrwKkr1Iv63Zr/d5x0tWMtGD9kI99Uyqksorz73FF9++WWNJgjNkca2OSDsTn3w9bq9zH9vlp3N8AteQmbSeGbMO09kW0OFz6gUErf1jxZivnXMyv1JTnUWU85l8+G0DoREa3h6XjLKMr54D42S+4e0bnCtivrG1WxOscHEseR8Difl1UnKFThfxCrVGlp26lk6Ri6bM5OYNXtRKG/BYh4PXAlAeOtCPH3e58zBNxxfQJJQa9yczhfys9JZ9dV7HI35G72uxC5FWUSxui4Wi3yhOEe2w4jq8tjmXX2vvoHstCQCwiLZu/F3giO7kJ44EXgBUAKfA0sZOLY9t0yfWaPIMxvV0X6FyjdO37zr6lo7Jl1Wg0fQvOge5UuorxtrD6WUallUxfpF80g4eYiQ6LaloddmYyEtO23kgTeGsfrrYDb8HMiGxSkg3wkcAKxhd2AVXlNpNHYdasQt9/P503dU6MRmi0xsRhGxGUUoFem0DPSgdZAnwd5uBHq6oVFd2oSkfPWxstfdHZfN7nPZdbLotC4gbRNdBTGrtcSs1gLJ3DI9iS9fjql2aUo3tYKJvaNq7dwRND2UCokRHUOI9vdgw7E0h1Er5dO4bPoWh7ZtKDMQ5aFU30tUu1RSzj3B+w9IjL4ziz8XtsZsvDjxLxsCO2D0TTWufOGMEoOZJEMJSbn1vzNX2cDvSEvAWRi6Uh1ASORPpMSNu/DOm6g0c+gxZCATHlxf4bpVpYJpVAoGtg1kcMcIOwH8+fPnM3/+fLFzKWhWjO7Xme/K2YzWXf6gOH8sK78IYdr7iRWqppgsMmsPpzD1ipaXPIYLrBxIyHXq3JFl+GamAovZk9AWr6FU3W/3fo8ov2bn3HFFNp3M4GQNo+qrisq1acCVX8QqlEr2bvyddT/Opc+I9zi0bQbQH4sZvHzP4un7Lfe8NpLQFgaWzTnNmYPWubssWzeVJIUS2WImKKIlWcnnHc4XlAqJK7q1I651OAc26UWKcjNCoZAY0j6IcD8t646m2gnul6X8vGvPxpUAxB7eDUB64lHgKPAdWs+56IpmAM+wfW02eVkW9MWbiD0c51CXx9mzXR3tV3CuPesTEFzh3HWhnSgcPIIGJ8Rby5QrWrDxWDqn0pwPLuU7alq8NV9StlgYNG4K+dkZ+Aaauf35VPqMyGfxB+0pzNuNQvEmFvObqN3UdpW5ynao7WuWVFlmuKyzB6xpXL7uaoK8rI4ei0XGLMuYLTJKhUSYj5Yofw9CvN1QlFPpL199rOzi61RSFv+cSCe7qOLOY23pNex69mxciSSNQpbfB3rh6XOcu18tIWbNHAqyMwht2Y47XviwUoHYFgEejOwUgn8DaaAIGpZ2IV6E+rjxz4n00ufchiPHwrI5MzEbDUgKBbLFgqRQYDEZiGy7mztfOs/KeSGs/jqY4Kg0/ILfIO7YnNLBzmQ0YDLoSx2vNoeHtfKF1ZG0ecX3HNj8JwPH3nbJ1Q3qmpo4cVQatwoDtkoThMlwF2bjM6TEhQOLgBeBBEwGauTYstEy0IOrO4fi666utgC+QODKtAj0wFCYU05D7Dxj7s5k+WehHNrqRc+hhRU+l1Ns5J8T6YzpJhZ6l0pWoZ6tpx3PGaw28SbgZ+B5Dm5+nxmj3y/dyVYrJXpGC7HbpkBtEjgqE0aGiotYo17Hvn/+uPDuWLaveZTta6IBN66/N5O+V+fjH2IGhgDWOXBBbhaDx0+lIDebQ1v+srbVYt2EykyKu/D7xXWAWinRI8qP3i388NaqWZCdaSfYm5KSUvMbFTRJ2gZ7cfuAlmw4nlaqx1YW27xr37+rnZ6jz8jxpVFfhbnn+PGdtZze78WxHaOAB4HJxKx+k5jV3VGq4X9rDpd+9vTBnU43JKuzWelM/7Jsv6krx6Rw8AgaBTeVkrE9wumc4c22M5lkFlZ0blTmLS3faToPKOb5rxOY80QamcmzQBqLUX93hcpcHz06sXSBCTUT15JlyC02Og1lPZ1mnVRqVAoi/LQEeLqhVkq4qRSs2XqAj956lb//Wo2upASt1p3h147l9umvsGxvYg3/es65uOBsDfyOLE8AzgG3UlSwnHnPXvR6p50/w4eP3FC6GC0b0eTppmRo+2A6h/vUWdsETRNvrZobekVyMrWATSfTKy15ahuc0hJiKczJwss/kNDoNuRnZxAQauK+2ckc3e7JinkhnN7/LtAFpfpdTIYT9L36BixmU6X9We2mpaQgr06qG9QVNXHiqN20dOo3lPysdJAktB5eGPUBSIonMRn+D/AD/kWSJiHL2/n/9u47Pqoya+D475meOumdjqF3RJoUxYYootjYXXdtWFbdd9V11wrq6rqWtaxYeH3trOiqoGIDdBeRooJKL9ICgZDey9T7/jGZkEkmyaRNEnK+nw8fk8ydmWdi7rn3nvs85yidngFjphAeFUtpYV7AY9LrFFPT431qWDSnAL4QXdnbS//NZ9s8F23e5LPLVcz6FVF8sjieQePKMZnrX73uyiqhV2yoHNdaweXW+Hz78QbrtFy7cDkv3zMe1EbQnqx3J3tQcmSraqeJjtHUcbD2rJ7aF7FfL32fXZt6U1VxBWiDgQx6pL/EVfeNIDYp1u97eW8uvfbgLZx61hxKi/LZ++M63C5XvfOGCIuBC0ek+Cy/bKhgrzg5WEONzB2Txt7sUr7Zm0tp1YmyAN5ECfjOAANPrSY0rSbx4n/J1XDgr8DjwPX0GfIemua5wb9yyQs+N8e9NyTP/vXv6/3dN3TjvLEZ2d7n//2+P/KvN15rdWJSoqzoUH3jw+kTF8aOYyVsPJDvd0cNdPlGWKSb5D4PEpt8Phm7b8RWsZV9W97hzhfHYzB5TkYWLFnT5DS61rI73RzKq+BQXu3ssqLQrsdWVYXBZMZmq6LEZSTX2fzK/t4psnNuvrfeLIf73ljN6w9v5NDO+dVvezfpozcRGRNBWdHkei1kaxfOPbh9E6uWLOLBvz/NpP5xbV5/SHRuA5Ii6BkTypq9OezK8j+zzl/tl7qGTCgnsdcPPD5/Ay7HbbicvyEmcRMF2UtI7JHtd3+uN1svwO4GTU0X96e5z2kq0Vw3RuVkHiQ7Yz8fPP85mXt/h1JvobkV8AHwBLCp5s6p5nYRk5jarCRWmFnP+cNT/HYFqdtqVO5cipPRKQnhWEOMFFeeuNmi18NFN+bw4p978PW7MfWK+np9vTuHZKuFqFCZldoS6/fnNdjJ0+2Cpf9IA0ygXYXBZPSJ80ohxa67GO/x8g/Pvst/3nul0fPHFa88SUH2UX5999PkZvbk6/esbFs/C6dDB3yHTn8tbtdbJPY8n3eeeLXJY7D3fOP9Zxegud31zhsSIy1cODKF8BY0/hBdX3piBL1jw/jhUAE/ZhTWlLfwNwMMYPjkswm3xtQkXuqe23mWAW4FLsTTzOdp9v18L3ecswp4B/DUtvLeHPcmjFa+vYizf3Uz5SVFXHLrAiJj4jlr3k28+ejtlBTkBnxu6v17Hza8T5skJmWvEB1OKcXQVCsDkyL46UgR3x3Ir7k7FEhGtDbvDlJamMlHL8Xz439+w5M32pn7P9n0H17Z7KRRW2ruZ2mId4ps3VkOtkrFp68N49DOycC36I3X4HbuIy75RHHXui1kf/rvp7Wmz8K6T95hxifvSN2ObirEpOfcocmkJ0a0qDWl13/ffx6XYyljztxPVPzDrFsxmoLjp3Jo5w/0GTaa2fMH8v2XS+sdaH3r++DT3cCfpqaL1+Y9UY1JSg34OdB0otm7X2/8/N9oWijZGdOApWz7dgxQhtK9wm1Pj+Xbj95i2/rtOGxUz9yZ1OyZO8lWC7NGNHxCK3cuRXeg0ynG9Irm6905Pj8/ZVQlY84sYfW/Yhgyvowe6fUTEXanm8+2HefyU3ugr7OUWjTueHEVmzMK/T7mSdLfDjwKXAf8gtPuuWvujXF94sJkuXcnFEh9uQ2fvlvvOOh7/mhi0+oq4FoemtcHSCEk3MX4mcUcz/gLiT3Kqs99q2q62gZ6DPZ37tw/IZxzhyZhlFpO3ZrJoGNS/ziGpESyZm8uB3LLfWaATbpgns/fTe1ZM/7O7eJSexOX0pO9P67G7RqOTn8bOt19OB1nVT8rB/gW+BLN/TZQUXMjEqj5m27OuWl7kS5aotMpqrCzeleO3/WVjfF3kNq9KZT3n0ug4LiJcecUc85v8ln+4o2NdqVp7h3+lswiaImGKrh7jATeRalTiE9dQv8RXzJh1qX1Pl/djjz5xzMJj7Sybd3qdu040NHdJSTmNF+Vw8XXu3OaVYSx4Q4a0fQc8BQHt58O9Ccmyc64c46w+4dr+N0Dj9Z01djw2bs13bq8d0da2m6yrjvOHeSnC1j9Lgf+9uXGOllpGhzaaWH9CiM//TcStysM2IbO8ArDJmYz5+ZbfT6ftytZ3c/VVBwZlmpl+sAEuSgNUEfHHJC4054cLjevfnuw3pLSyjIdj8/vhSXUzR8XHfa7VAtgXJ8YJvWPa7KzpfDQNI13vj9CdkmV38e3r6/k1QeHotQHaO7L/S7BvXRsGmnRocEcdlB1tZjz6dYs9maX+u/02MAxVul03L5oGRs/e5ftG76lOG8kMA+YCUQApcDnwDL0xs954tPva57bkuN2XXqdYmyvaCb0i0XVraYuur2M/HLW7M0l30/ZD3/8ndtFRMX6nKuNn3kFlWXJ/LymBKWbguaeCPQD8oAX8HTiavyGfXP+xq89vQ+RlsCb2kibdNHlbD9azDe/5DZYLb0ufwcpAHuVYuWSWP77fjRoMGJKKdPmFvq9u9fY6zT3fduat8XeiVkO4cAc4NfAmRjNhcy7K4u1y68NONnUKzaUFS8+zBuvvoLJZMJut7dLe+WOPvGRmNNye7NL+Xp3DpUN1OZpqu2502GvlVjR4Zn+egeeooqFpPZbyzUPDq1JvPqr71N3aVhj7SYDbW0JJ4rtoWk1s3s2r/4ooH0575iRTasj2fxVBPlZJkxmN9a4b8k9ejd642bcTt8kTlPtzhuKIyaDjukDEhicInVDmqOjYw5I3GlvGw/ks2G/ZylW7TiUdbAXL9+dxtSLC5l9o/8Tb51SzB2bxiP33MHLL7/cLse9k8nWzCK+2pXj97HyEh1P3dSLyvICbBXpGEy2eknsJKuFK8f1DOKIg6+rxRyT2YKjgYRL/SYBZiwhYcz/22s8e9szuJyXApcBsXhmNSwDlgNfYzTr/B6Pm3PcrkspGJgUwfi+sbK8UjTK7dbYklnExgMFfjvENsXfuRrg87Ot68opLfgdnvNZO/AmsBGd3kZohJ7KsmO4nEcxmA4x6FRPXcarFy4K6LpMEjyiWyi3Odl4IJ9dWSUNFvUL9K5AYY6Btcuj2PiZlaoKPX2HVXDm5QWk9D3EW3+7ncO7t9RqLd7w6zT3fdvSv59ZwIbP8oGrgdlAKHAAT0eeZ4ACACbOurLJC9Qkq4VLx6Rx2aVzSU5O9qnbUXupR1vo6BMfiTmtU2F38t2BArYdLcbl9t0P6yYm6s5UqVtc+YRxeBI9lwCg1Apu+NsYThlVUa/NsT9NzYjxqntSCdSbHRTI7B5Ng6yDJratC2fbunCOHbCglEb/kRWMnVHC8Mll/Ovx3zeaxPGnsTjy6po9nD8smRhZ1tBsHR1zQOJOe6tyuPi/bw9id7rrxaEP/pnA+hVWbno8k/4j6i83bmi/k+XJ9VXaXby+/pDfiyVNg9ceTGHX92H0HnwLSb2K/Ma/84d7lv6ezLpazHnrq5955pH7G0y4vPvUo3z3ZSY6/UjcroHAUHT6EbhdISidDViG5n4D1H+IiouhKDcLpXSA/5m3EPhxu7a+8WFM7BdHfIS5ub8O0Y3ZnC62HCnmx8OFDd6gbClvEih99DUsfSqXyrKLAIufLY9iCVtPVfm/GXdOJFfccVe9LZwOqCzTU1Who6pcxwXDU5l+euAVdCTBI7q0SruLLZlFbM0sqlcXpDl3BUryc3j94ftJH/MqP3yZRmGOkaj4rRTlzmPsjFOa7PJT29F9O3np7muxVZbjtNuadTeiuWyVik2rI1nxShW2yl7o9MWYLJ+g1BKqKlbVtJCsq6FkU5hZz5XjehLRjCxxa3T0iY/EnLZRVGFn3b58fskp5U/nNzCFW+mYOOuKBqe8Ou024lJ6UZyfXX1nMJ3Y5EcoLZhNRamRhDQ742cWMXxyGTFJTj+j8GhqRkxtNcu/UGiamxFTziXcGsP6FUt9uizUNmr6BUy95H7yjqZxcEcIu38IJT/LhFIaaekllBe/zm/vm0iP9Na1/G0ofv35gUe4ePIQDFJjoEU6OuaAxJ1gMFss2G3+l4VGxx/H5VL86aUMLGG++7l3v9ux4at2XZ58Mli9M5t1W3/xu4R07UdRLFuUwIXzc5g2t8jv86NDjVw1oTe6k3x5aVeLOZ9uzeLRe26vdWw2M/DU+0jufS37t4ZweI8J8DbbyAa2V//7DtQKFGU1iZroxFQGnTqlyeNxc47bKVEWJp8S77ehgBCBsjvdbM0sYnNGYaNdYlvqtQdvISwyjWGTrmTT6lXs+XErfQZPYef3h9DcM4Cz8XRQdeJZymUgJDwGt0vhclJdhPyEPn01DuwPPFY2FHekyLLoEkJMesb3jWVsr2h2Hy9lw/58ymyei7/mFE5eueQFMnatIaXvvZQUfgxcQ1Hug8AWNq1+CVgAVAVUgHnDZ+9RXuyZMdPWBZtL8nN44693Me3SlziwLY3vv7RSWaYn7ZQqpszJYuSUMgymcbz/7Kds+Mxdc5DV6fS43a5Gu4PpdYpZw1OCltwRJ4+oUBPnD0/meHE0luVr+d8nHmo0IeotaPfag7f4FEnc9cOamv3V5fiFfsM+ZPaNI9jyTTjrPoni48UJfLw4gZS+VQydWM7QCWWk9LOhq3UcbKzdZF3+ijRecusCzpp3U63kShgwCtRo0Eazff2Z/PSfOABMFjd9h1VyxuWFDBlfxsq372fDp0v57osr6JG+sFW/U3/xa2DPBC6bOqxVrytEd7Bv334uvfpmfly7sl4cKjh+nH/e3oOPXo7n8tuzfZ7n3e/stirMZgtVVVVERkZKcqeO48VVbD9W7Ldo6NH9Jj5eHMfg08qYekmR3+crBWcNSTrpkztdVUlhPsMm34vbdR07Nyax+wczv/zspteAKmZcWURS7yy2fPMYuzd94LN/VZSOIjYpzW8B28aOx4Ect2PCTEzqH0f/hPC2/bCiWzIZdIztHcOIHlE1HbcaWhHSErX/pgeNu67m6xNlNa7BaR+JTn8+MYmj6DN0BJbQEnQ60Bk0LKFuLGFuQsJcWMLcXDklkbZIz7TqFZRSlwILgUHAOE3T5FaVaFcGvY6hqVbSEyP47mA+Px0uwuXWmuxQVXc6trfiuVKL0Bs/wGn/C3ATqF+T0GMtZ15uJWPXS5QW1l+/729qt9Nu8+kW0ZzCy7W3rSxLZcfGML79qJKi3FUc3BGGTqcx/PRSTr+oiN6Dq3yWr9T+3Eseu4PjGfuaTDZNGxBPShe+IyJxp+MlWS3MP2csK9+O46cAEqt1T+pee/AWBp061Wd/NZo0xs4oZeyMUnKPGtm+PpztG8JYtSSGlW/HYjS7iU12EBVfStbBz5h68QT6DDGT1NuGOaTxg3Xt97/4lgUU5RrYvsFM5i+DOLhzAQ7bIqC6PoQG5pA8NG0dsJrhp0fwm3t+h17v2ff//YxvHGmojXtzYkBpUT6nXziPG2+4gW8+eYfs48cb3V4El8SczqtHWip9U+L43k8cioyp4ozLCvhqaSwHdz7KzY9f6LMveo+f02dfSfYPn3Jc9jsfmqbRMyHKp07LiY4xocSl5hIW4eaKO7IbXFY7ple0zMBogfaOOWVl8MUHIRTlfEzmPgsmi5tx55QwanoOvQZV1SpObuLAVle9G6i//suTNa/VWEKnOcfB+AgzI9KiGJISKQlB0eaMeh0T+8UxNNXKt7/kBdRApDVNdLw3EVyOCgymTbgc60kf7V2S2PB7jxvfNsmnVi3RUkoNAtzAy8CdgQYgmbYs2kpBuZ01e3M4lNd4x62GlkEonY4fv/q4enpqP2KSXqU4/1RcDh3RCQ5GTCll2KQyUvqeuIgMZEmYtx7AmDNnU5B9tNHg8P5zC1m/4guSei3leMYMPIVodwBfV/9bg8FU0WRdn0Cmvg5LtTJjcGKTv9e21pZTl1sSdyTmtI+LL76YyJh4+kyezaoPlwRUc6a5Sgv17PohjKyDJnIzTRzcXkJleRxwoi5NbLKd5N52zKGl7PnxB9L6j+LInr2knTISTbPgsCucdh0Om6K00EB5iWfauVIaJssRImKOMPi0KPKPfcSOjU/j6Y7gS280cf+bXwW8HDTQ4ut6nWJ4mpXxfWOxGPUNbieap6NjDkjcCZaLL76YCn0EQ8+8pN6xz+mAh+bpKSuOYfQZC7jw+sv8nrD3ig1l9shU6VJXy9bMIpZ9u71ezLPGJpJ37C/Ajdz4WCbpo/2ff8VFmJk3rme3+Z12pZjzxBNw112Q3MfGxPOLGH1mKSFh/pcrN2dZVV2NHQeVgmSrhf4J4fSPj8AaKrPKRfAcLapk4/58jhZV1qst6dXaJjot2Xc6VZFlpdR/kZMe0YF+qe7009j6Sn/F3UoL8+rtfFfc+QI7NoTx85oI9mwOw+VUKKURm+wguY+N5D529m7+lEO7dqN04WhuIyl9BzH90jEs/ccZuBy767230ul46otdPj/zzASyA1cBj+PpRrAIeAyjubDZXQYa4+lAEMlZgxM75GSrPdamNyfuSMxpX3anmzV7c9l+tLjd3sN35pwe6I2n6OMohk26nayDJgqzq3DYi9EbNFzOckIjQ4lPTcRo0jCYNIxmN2ERLlL62UjrbyPcmsnSf/yPz8Ve3QSu0unR3C7GnnUR8/709yaLRAZafF0pGJAYwcR+cXJi2w46OuaAxJ1gyiuzsWTjYdy1zmlP7It9gJ+BbcBUwOW3EcGg5AjOGZIk7ZeB4koHb2/M8BSwro55J64XZuPpmPQ48Gf0RhNPfLrN5/l6neKKcT1IiPBXePTk1JVizvbtx5k99wF+s/C3WGPbtmYkNH4cfPrzbQxJtTKmV3SzLmSFaA8Ol5ujhZUcKazgSEEl2SVVQWmiU7cDrffrP84e1yYJnqDV4FFKzQfmA/TseXK3ShTBd0piBGnRofx3Tw67G5h2528Zl//1wO6apSIVpTr2bw3h0E4n33+5j2MHJrJ9fTiadhUAmht0OjtZB2HJ303ALgzGQpyONXhm4RQDJWjuEm4/+3Z0hjAu/+Mz2Ct1TLtkFxs+q6S8eDCwHtR5oP1IXGpv8o81vewlUElWC9MGxJNs7V7TpCXmBI/JoOOswYkMTIrgv3tyyCur342uteq2bTWajzJs0mAunD+Dv14V73MwdlXXZq4ogYwSQCkWvrPWzyyb5+vVlfBOq/V23PIWMN+0ajmbVi2vLiLd8HLQ+uOsXw+rV2wok/vHkRDZfS5+uguJOx0jLtzM8B5Wfj5cVPOzPzyztLoRwjGc9vnAUjyrXu73u7xyV1YpISYDU9Pb/oK3K9E0jVU7s7E7PTM6vOdOQyedxXvPvEjh8f8DNoNaABqMmjaz3muM7xvbrZI7HaklMeeFFx7i4N5XWLWkrEUzE5ri7zg48vSzue+hR5kxpg+hJikBKzoHo15H77gweseFAXCkoALDB2t5/R/+a0y2ldp1zYCar/84e1ybvH6Te5hSajXgr+rcvZqmfRToG2mathhYDJ4Mc8AjFCJAISY95w1LJj0pgq935dQUYfZqTlFWr9AIN8MmlbNn80IqSpcyaNxsImLyuPz2J4hNjEVv1FAK3C7IOmQiY1cIaz7YQ+7RwcAs6u5ibie888SJ75XKBa4BXvf0GwXyjh4CQHO7mTjrSkoKclu0DjTcbGBS/zgGJUd0uTuSbRF3JOYEX4+YUH51Wi9+OlLIxgMFNRcIbaGxYurek8mt61b5JHoMJjNRcUnkZx32SeL4q8lV+2KvtCifU8+aQ2lRPnt/XIfb5Wq0iHSg40yLDmF831h6xIS22e9FtA051+n6JvSNZc/x0pqWuLUbIeiNy3A5/g+4B/gao3mD3xP2HzMKCTfrGdMrJsij7zx+PlLEkYITy668505uN7gdY/G0A74SNE8S3Jv89s7kSbZaGNsrugNG3rV0RMwJCQmhqqqq5vvG6si1Ru3joNFkxmm3M7JfMhdOGNJm7yFEe+gRE8rvZ47lm6XxAdWYbK6GasJ6v7aGvIPFYqGysrJV79NkgkfTtBmtegchgqxffDipUSGs2ZvLzmMlrXqtu84fhtNxYjbCptXLAXj8+kk+S650ekjtZye1n509m/9K+uh4SgoL2PbtGiAaiGDY5Lmc8+vrMIdomCxuPnv9Yb77/A3iUnsTl3J6oxeS7z+3sN5Mg8YMSYlk2oAETIau2WJZ4k7XpdMpxvSKYUBSZE0hO3cbLAUG/7PwoHYxOztKp0NzexJLTruNvGMZgO+JbFOzbLwXNO8/uwDN7W72Ab7uOF1lBVx2ag8pNtqJSczp+ixGPRP7xTKmX1K96fUuhx24DZgIvI3DNrLB/XntL3mEGA0MTokMyrg7k8JyO+v21a9BBrDmg2iK8+PpPXgRlrBk9v54ALfLVbOMddS0mZgMOs6RrlkB6YiYc+DAAe68806WL19ORUWFz7GvNQVl6zIZdChbMVddcz1/vPVmFi9eTFZWVht9CiHal06ncFUUcd38Gxh33mW88+ar5GZnN/3EANQ9/1Q6HaDQqjsgXzRnDs89/Y9Wv4/MkRMnJYtRzzlDkhiQGMHqXdmUVjmbfpIfI6fOrEnq1Ka53dx+9gC/dz28F4evPXgLky6YU+ti9HtS+l5VL3ubd/RQzayduheSdRNMTd1tMRt1zBiUSHpiRIs+rxBtJdxs4NyhSUzoF8tPhwvZcayk1TN6GpuF502qZB85QFlhPpbwCMqK8snPOoLmdtdLnDY0y8bfaza0HKuxcRp0iv4J4dw172xZiiVEkAxLtfLCx+t46e8L6yVwK0qLsYS+wbZ1jxAZ8yUlBf6n22sarNqZjYbGkBRrkD9Bx9E0jZU7j/ttIVyYY+CLN2MZOqGMqxeexQfPfYvb5ZkpVXcZ651tcPdZtI/k5GQiIyOpqqqqnllz4tjX3BuJdYWa9PSJC6NfQjg9Y0L5/fTPah5btGhR230IIYLgww8/rPn6utnTqXK4yCmxkVVcyf7ccrJLqhp5dn21E6h1zz/hxPVfZEQkSUn+JvY1T2vbpM8B/gnEA58qpX7WNO2cVo9KiDbSOy6M30zoxdq9eWw/VkxjEwlq73x/vepMvwW2vPytx6x796Ohi1F/swdCI6I4ZdQEpl78O58LSW+CSafT43a7Gl0HmhodwrlDk076onUSd7oWa4iRaQMSGN83li1HitiSWUS5reFi6C3lrzPB+88uYMOxw36TOIEkb1qyrDPJamFwciQDkiKkK9ZJQmJO16GU4rIpw3lzUUSDrZ2/WZbP8hdHMWPeW3jq5NXn1jRW7sgms7CS6V14NmxzbM4o5FiR/4uWj1/2xM2Lbs5BKfwuYzVbQph7ycU8+eSTfl9DBK49Y052djY33ngjA6fN4d23XmP9iqX1lokEumwr3GwgPSmC/gnhpFgtXa4cgBCBshj19IwNpWdsKKf1jeVwfgU/HCrgcEHjXZy9atfcqX3++dqDvwfg6gWL2PjZu2TnHG+T8bYqwaNp2jJgWZuMRIh2YjbomTE4kcEpkfx4uJD9OeV+l4zU3vnqT6HTY41NoCg3C6V0fu/4135+3bsfPskfPzU6hoyfXvOcS25dwF2zhnP72QNqnu+uvkPmsFXVe9+4CDPDUq0MT7V2i2nREne6JotRz2l9YxnbO4Y9x0v56UghOSUNJ1HbQmNJnJYkbxoSYtIzMCmCoalW4sLNrXot0flIzOlarCFGzI7SBvf9ybOL2LLWxPvPRZDcO5Pegxue8brzWAnZJVXMHJZ8Uu/bWcWVbNif7/exvT+GsmVtBOf9No+YRM9s6LrLWI0mMw67jcjItrn73N21Z8zxzkz4dGsWl9y6gLPm3eRzvmswmbGEhHHD31/z+3yTQUf/hHAGJUXSIyZEkjqiW/Ime7JLqticUUh+uR2H043d5cbudNe0Xm+o5o7BZOaSWxdw35tf1Tx2ya0LuPb0Pm0yPlmiJbqNlKgQUqJCKK1ysC2zmG1Hi6mwuxosuKp0OtA0DCYzLocdnV7PpAvm1TthbKpgK9RP/jQ1e8BfgmnAmEmER8VSWphHiEnPgKQIhiRHyvIP0aXodYrBKZEMTokks7CCnw4XcSDXf9I1UA3VDmjLJE5dSkHPmFCGpFjpnxCOvhskV4XoKlZ//gkf/XyUA7nl9fZ9nQ5iEu/j4Pa/8tqD4Sz4l6eOXkNxJL/MztLvDzNtQAJDU7vmkq2srCyuuOIK3n333XoJmKNFlSz/6ShOd/0Y7HTAh4viiU22M+3SwnqPe89l7rvjVj557y2ps9IF+bvpWGa3sWHFUi77w4PEhJtIiDCTEGEmPsJMYqQFo/7kn9EmRCASIy3MHJZc7+eVdheHCypI+vI7nnnkAX5au7LRjlx6nSLEqEffRglTSfCIbifCYmRi/zhO6xvL1swiHvrX17y/6G9+1+vHJqX5JGG8J4pNLbny7ryBJH8C6cTjctiJSUzltgceY2SPaLmgFCeFtOhQ0qJDKa50sOVIETuOlVDl8F2+5b3omnPzvSx74RG/BSAbmz3XlnRKkRJloX9COP0Swk/65ZBCdGUzBiXyVnFGTVctqHtDpojSwre587y/YDA9w7izL24wjjhcntbhOaVVTEtP6HKzZR9++GG+/fZbHnroIV544YWanx8pqODjLccarI/27UfR5Bwxc+1DRzGa6ieArl7wPGN6RTMlPZ5zp45vt/GL9lValA9K+T1fbYuOPkJ0N94b8QOSRvDNuyls+tqO2WzB7rAzuFci150zGr1SWIw6LEZ9my/plwSP6Lb0OsWontEMSBrHuvdi6rXD867Xh8bv/gfSvrmhbj2N8d4ZmzTrcnZ8/SHu8iIuP7Vnm3x2IToTa4iRKenxTOgXy66sErYdLa5ZvuVN3rz92J3kHN7frHbnbSHCYiAlKoSeMaH0iw8nxCR1dYToCsLMBs4YmMCnW0/MKvE9Ji9B6S5Gcz+E0/5FzdT5xuLIliPFFJQ7OH9YcpeIBXXbYr/44ou8+OKLWCwWdmfm8cmWY36LKgOU5Ov54s1oQiO+pUe6wlMOxldCpJlJ/ePaa/giCPQ6xd9eeINEQwX/+Ov9fPTRR1RUVBAaGsqcOXOkppIQreStezV//vyajnLt3VVVEjyi2ws1GTA5SvntNdczaPocPn73rYC75Xg11b65qW49dUWHGnn6f9+iR0woPaJDCZl/YYs/nxBdhVGvY3haFMPTorCEhGCrdWGSnbEPaF678+ayGPXEhpuIDzeTHGUhJSpEZukI0YWlJ0awP6mM3cdLAX/H5BswmvagNy7H6RiJ017cZBw5UlDBO98f5sKRKZ2+Lk/dttjei/Zb//IgH/98zO+yLPDMnHzyphIctp7Yq65m5dun1pvVZDLomDk0WWYTd1ERFgMT+8UyNNVKmNlzOWi1WqmqqsJisVBVVSU1lYRoA7U7cgWro5wkeITAd+e7ZvZ09h4vZW92KYUVjoCeH0j75qZaLRv1irG9YxicEikXlaLbO1h9YbJs2TKf6eEms4UJZ57HxTfdjWaJalECFTzdPxKtFpKtFuLDzcSGm4iQ/U6Ik84ZgxLIKq6iuNJzPK97TM7c9xQZux4BXkJvvDqgOFJc6eDdH44wfUACg5IjOm2h2dptsb0X7XZlZuNxd03dM3+1hxbOewC0r4G/AvtYv2JfvVlNU9PjiQ4zddAnE601Jb3+37e/mQZCiK5HEjxC1BEXbiauv5mJ/ePIKaniQF45eWU28kptFFU6Gm217k8ghV5PSQxnSnq8JHaEqOa9MLHZbOj1elwuF3q9HqfDzuBeSdw261QcLjdfPlvJZb+5hjPnzGP5O29SlJ/DyB5RGPQKg06HXqfQ6zztk/VKEWrSk2i1yL4mRDdhNuiZOSyZ9zYdweXW/B6TH7/+fzmecT0jJp+KTv8nfv7mc87+9e+JjIlvsPiy3enmyx3H2ZxRwIR+sfRPaLgbV0fyXrRfd931LHziWXYfPMKEWicytWuYfb/yQ5x2I7AV2Af8rWa70WdcUDOryds1UJxcOmKmgRCi7UmCR4hGJERafLpUOVxuCsrtOFxujHodRr0Og16h8HTayCm1kV1SRXZJFaVVziZfPzbcxLT0BHrGhrbjpxCia/JemOzevZvs7GwSExMZOHBgzV1Fo17HZ58sr9n++ovO6KCRCiE6sySrhYn9Yln7S57fx/+0eCofvVTIN8v60WvgXCpLl9fU+2qqiHtemZ1PtmSREFnAxH5x9IkLa+dP0zwffvghVQ4XK7ZmMe3qu5lW/fOG2vfCs0Av4HSgoqajqHdWkzXEyBmDEoL6GYQQQgROEjxCNINRryOxgbbkERYjvWud2LncGnanG5vThc3pxu50Y9ArTHodZqMes0EnrSaFaETtu4lCCNEaY3pFc6SwgkN5FfUeUwrWrUgBXiNj9zzgC9aveKtW0qPpIu45JTaW/3SUwSmRnDkwAUMnOb7nltpYsfUYRXWWnPurYdZr4J/Yt+UG4EmU2oimwfDJZxNujaGkIBe9TjFzWDJmQ+cvMC2EEN2VJHiEaCd6nSLEpO8SnTaEEEKIk5lSinOGJLFk42HKbPVn2N7/5iqWv/QUW75JQNP+D52hCGvsj5QW5uG02wIu4r7zWAl5ZTZmDU/BGtJxS0FtThcb9uez5UhxTb2d2uoWnHbYLBze8wfMIRmMnr6diRcsq6kb6F3KNqFfLElW/ze5hBBCdA6S4BFCCCGEECe9UJOBc4Yk8eFPmfXq6UXGJhAabkHT5oD6L27n+xTn3Y3b9XSzi7jnlNh45/vDnDc0iV6xwV2ypWkaO7NKWLcvj3Kbq9FtaxecfuuRKHIyo/iff5bRc8A9gG/dwMRIC2N7Rbfr2IUQQrRe55g/KoQQQgghRDvrGRvKqJ7+ExWlRflMumAWNz9xHHPIWtyupzCHfs4Nf/uQibOupCD7KM/f8esGu2HWVml3seyno6z9JZcjBRXYne62/ig+HC43e7NLeW/TEVbuyG4yuQOeJhCX3LqAwpwx5GRO4fSLjvLx4kvrfT6lYPrA+E7bLUwIIcQJMoNHCCGEEEJ0G5P6xZKRX05+md3n51cveJ67Zg1n3Sf/qv7JHdgqHmPRHYfRGw5y2rm92fDp0gYLLtelabDpUCGbDhWiU4rYcBPJVgvJ1hB6xIQQ0cpufi63RkZ+OXuzS9mfW96iJFLmL2aWPplEcl8bDvu9fgtKD0qOJNka0qqxCiGECA5J8AghhBBCiG7DoNdxzpAkln5/pF59Gt/iw0+hN27G5XgDl/M/rF9xK6A1WXDZH7emkVtqI7fUxtbMYgCiQo2kRYeSFh2CyeCZVO8Zjoamgd3lxunScLjcOFwaVQ4X5XYnFTbPf8ttTvJzsv22cQ9Exm4Li+9JpbIsk4rS6WQdOAj4FpR+5ovtTO4f16zXFUII0XEkwSOEEEIIIbqVxEgL4/rEsPFAvs/P6xYfdjnWMHLKfRzY/gdKChYDwzCY7mH45OlNFlxuSlGFg6KKYrYfLW7xazTVxr0hB3dYWHxvKuFWFzc8msM3y4awbX1WTUctb0Hp8X1jCDPL5YIQQnQVErGFEEIIIUS3c1qfGA7mlZNdUuXz89rFhz2dpI4zZPxTbPhsDHAHTvtg9IZ3mz1jpi3dNWs4Trut5vvmzCratyWEV+5PxRrn5KbHM4mKs/oktbwFpXv3SGVkDymsLIQQXYkUWRZCCCGEEN2OTqc4Z0giBp1v8WBv8eHUfgO55NYFXL3gecqK85h0wY+ce9VPKN0UtnyzgOMZpg4auWcp2ejpszCaPW3LjWYLo8+4gPve/KrR5+38Loz/vS8Va2wlIeGz0emygBNJrT88+x4TZ11JaWEeU9Pj0euksLIQQnQlMoNHCCGEEEJ0S7HhZqakx/P17pxGt7t6wfM1X6ePzuK1B1N45lYd51+Tx6QLitDp22Y8Jfk5AdXUqbuUrKk27poGq9+J4Ys3YknpZyO5zx1sXv0lK9+OZu5tC30+3yW3LqBfQji944Lb4l0IIUTryQweIYQQQgjRbY3oEcXY3oEvReo9uIo/Pn+YPkMqWfZCAv+8vQdZBwOfzVOSn9Ngu/XaNXWa4m/WjT+2SsUbDyfz+etxwFKO7oth06qX0TRPwejbzx7AXbOG12xvNuqYPqDjlp8JIYRouVbN4FFKPQFcANiB/cDVmqYVtcG4hBDCL4k7QohgkpjTPUzuH0e5zcmurNKAto+KdzL/0aNs/iqCj16O56mbexIZ8zq/f2ogsUmNd52qncQ5+1c38+ajt3N49xacjhNt2wOpqVN31o0/uUeNvLYwhewjJi6cn8PR/e+x+atKdDo9brfLp6Cy19T0+Fa3cBctJzFHCNEarZ3BswoYqmnacGAvcHfrhySEEI2SuCOECCaJOd2AUoqzBifRMya0Gc+BsTNK+fMrh4hNWkdR7jU8eWMvnrjhH35n59x1/jBuP3sA61e8UzN7ZuGVp3Ng2w8MHn8GYdYYDCYzEHhNnYa43bB+hZWnb+lJSYEBpTufjxcnsvmr5dWPuwBw2Kp8lnb1jQ9jSIq1Re8p2ozEHCFEi7UqwaNp2kpN05zV324E0lo/JCGEaJjEHSFEMEnM6T70OsWsEcnER5gb3a72Equ7Zg3ngUtPIffoFOAsbBVVZB18iYVX7KG8xPc0e+TUmQDo/BTs2br2C8qLCwKuqdOYo/tNPPc/PXj/uUR6nGLjj89n8MBb9/gUZVY6PQNPncLYs+bULO2yGPWcOSix2e8n2pbEHCFEa7RlkeVrgHcbelApNR+YD9CzZ882fFshRDfWYNyRmCOEaAdyrnOSMxv0zBmVyvubMykot/vdxrvEasUrT5LSdyCRsQlsX7cKWA0MA+4H/sT9c/PR6f+M0n2Iy3Fi6Zd39kxDnHYbSqdrsKYO+C/GXFWu48u3Y1i7LJrQCBfz7spizJmlKAXgW5TZ5bATk5jK3NsW1rzm9IHxhJul/0onIzFHCNEsTUZxpdRqIMnPQ/dqmvZR9Tb3Ak5gSUOvo2naYmAxwNixY7UWjVYI0S20RdyRmCOECJSc64jawswGrhzXk692ZbP7+InEzF2zhuO022q+37R6uc/zlNKhaZXAPRiMyzCFvEVFyevoda8SZt1NZdkXuF1rQG1k4NiB5B07TN7RQ9XPcwP41MRpbPaON8n06auv03/4w2xdF86eTaE4HTrGzyxi1rV5hEa4fZ7jLco8fublbPzsXZ9lZP0TwhmYFNmK35poDok5Qoj20mSCR9O0GY09rpT6LTALOFPTNAksQohWk7gjhAgmiTmiLpNBx3nDkkmJCuGbvbk43Rr3vbGajxf/nR//s8LvczQ8fxpK6XA6fiAkfCpX3Pkfjh9KZPNqA27XLcDtoMHhPYcICd/MoHEH0Ok3smPDh57n1Vqa5XSAvUqHrVKHvUrH8YxS3nj4cTwrdiYD1/HDyin8sNIAHOH0OQ7GnllCj3Sb3/E1VJQ5zKznzEEJbfOLEwGRmCOEaC+t7aJ1LvBnYKqmaRVtMyQhhGiYxB0hRDBJzOneRvSIItlqYcXWLIj1LHMC/M66qSgtJjYpjfEzL2fJY3dwPGMfh3c/zNzbFpKfdQvh1hR6DrqODZ8eJT8rneLcOeQf0wEudHoHOp0Bt8vNhs80Nn5uwu1Sfkb0VvV/84FD6PT/4JRRx7nijkuwxja/Xk+42cAlY9IINcnSrM5CYo4QojVaG82fB8zAKuVZ4LtR07QbWz0qIYRomMQdIUQwSczp5hIiLcw7rSefbcuitCifSRfMo7SogK1rv/CZdfPrvzzJXbOGs+6Tf9U819vuHKVY+M5aImPiOe0cz2wZh30/h3ZYOLA9BFulpyCzUoACpcoxh7gxh7j5ePEC3K4iIAfIrP7nue7X3IrYpCtalNyJsBiYOyaNqFBTq34/os1JzBFCtFirEjyapvVvq4EIIUQgJO4IIYJJYo4AT4epi0amEvvqEn7MKOS1B29h0gXz6tWz8S7j2rZ+NQ5bFUazBWtsIvlZh1n59iKfosZGk8Ypoyo5ZVSl3/f0FlK+/pGbePtvd2KrLK8pwByb3Iu5ty1k27erKCnI9Sm6jKbx5qO3M+fme1n2wiM+hZi9rCFGLhmThjXE2G6/M9EyEnOEEK0h8zGFEEIIIYRogk6nmJoeT2yYCf2Di3C5PaVRateziYw90a0KwGGrIu9YBnBiNo/BZObxFVubfD9vIeXlLz5KeXEBQE0HrPRRE2v+Abz/3EIObt/EyrcXAXBw+ybefuxOcg7vr5dYigr1JHciLZLcEUKIk40keIQQQgghhAjQ0FQrMWEmVmw9Rrmtfstzb7eqoZPO4oN/LiQ/6wia2+3TIasxdbt1ZWfsq/m6bgv1utuuX/FOvefVTiwtWfcL5w9LJkzaoQshxElJ19EDEEIIIYQQoitJiQph3mm9SI0OqffY1Que55JbFzBg9ETSR04ATcNgMvt0yGrMfW+sZvT0WRhMZp+fG0xmRp9xAQv+9U1NRyzvtkazBQCl04HS+X3eu19vYu7oNEnuCCHESUwSPEIIIYQQQjRTuNnA3NFpnNo7xlMc2Q/vbJ4/PPseE2ddWTPzpjHeZV4uh92TsMGTuHE57PUSRLWXhBlMZjS3GzR3vecN6JHAxZOGotM1MFAhhBAnBUnhCyGEEEII0QI6nWLyKXGkRFn4ckc2VQ7fJVvemTbgW6unKd7EUPaRA5QV5hMeHUtij741xZz9bTt+5uW89uDvAYhJSqOsMJ/ouDgmjB5OYV5OCz+hEEKIrkQSPEIIIYQQQrRC3/hwfjXezLpf8tibXYZb01r1erUTQ83Z9r43vwJAr1OM7R3NaX1i0cusHSGE6DYkwSOEEEIIIUQrRVqMnDcsmQn97Gw6VMjOrJKaTlvBlGS1MGNQIvER5qY3FkIIcVKRBI8QQgghhBBtJCrUxIzBiZzWN4YtR4rJKCgnr9Te6lk9TYmwGBjVM5rRPaNQDRUFEkIIcVKTBI8QQgghhBBtLMJiZPIpcUwmDpvTRVZRFceKKsksrCSruKrVCR+dUiRbLfSJD6N3bJjM2BFCCCEJHiGEEEIIIdqT2aCnd1wYvePCALA5XWQWVnK4oILMggqqHG6f7ZUCk0GHUe/9pwg3G7CGGE/8CzViNug74uMIIYTopCTBI4QQQgghRBCZDXr6xYfTLz68o4cihBDiJKLr6AEIIYQQQgghhBBCiNaRBI8QQgghhBBCCCFEFycJHiGEEEIIIYQQQoguThI8QgghhBBCCCGEEF2cJHiEEEIIIYQQQgghujhJ8AghhBBCCCGEEEJ0cZLgEUIIIYQQQgghhOjiWpXgUUo9rJTaqpT6WSm1UimV0lYDE0IIfyTuCCGCSWKOECKYJOYIIVqjtTN4ntA0bbimaSOBFcADrR+SEEI0SuKOECKYJOYIIYJJYo4QosValeDRNK2k1rdhgNa64QghROMk7gghgklijhAimCTmCCFaw9DaF1BKPQJcBRQD0xvZbj4wv/rbMqXUngDfIg7Ia9UgO4aMO7hk3MHV3HH3ass3DyTutCLmQPf5/9JZyLiDqzuMO+gxp3o7OdfpGmTcwdUdxi0xJzhk3MEl4w6uNrm+UprWeFJYKbUaSPLz0L2apn1Ua7u7AYumaQuaMagmKaU2aZo2ti1fMxhk3MEl4w6u9h63xJ2WkXEHl4w7uNpz3BJzWkbGHVwy7uCSmNP5yLiDS8YdXG017iZn8GiaNiPA1/oX8CnQpgFICNH9SNwRQgSTxBwhRDBJzBFCtJfWdtE6pda3FwK7WzccIYRonMQdIUQwScwRQgSTxBwhRGu0tgbPY0qpAYAbyABubP2Q6lncDq8ZDDLu4JJxB1dHjlviTsNk3MEl4w6ujhq3xJyGybiDS8YdXBJzOh8Zd3DJuIOrTcbdZA0eIYQQQgghhBBCCNG5tWqJlhBCCCGEEEIIIYToeJLgEUIIIYQQQgghhOjiOk2CRyl1rlJqj1Jqn1LqL34eV0qp56of36qUGt0R46wrgHH/qnq8W5VS65VSIzpinHU1Ne5a252qlHIppeYGc3wNCWTcSqlpSqmflVI7lFJrgj1GfwL4O7EqpT5RSm2pHvfVHTHOOmN6VSmVo5Ta3sDjnXKfDJTEnOCSmBNcXTHmgMSdzvr5JO4EV1eMOxJzOieJOcElMSe4umLcCUrM0TStw/8BemA/0BcwAVuAwXW2mQl8DihgPPBdFxn3RCC6+uvzusq4a233NfAZMLcrjBuIAnYCPau/T+gi474H+Hv11/FAAWDq4HFPAUYD2xt4vNPtk238/6TTfT6JOZ1v3BJz2nzsEnc62eeTuNP5xt3Z4o7EnM75T2JO5xt3re0k5gRn3J0u7gQj5nSWGTzjgH2aph3QNM0OLAVm19lmNvCm5rERiFJKJQd7oHU0OW5N09ZrmlZY/e1GIC3IY/QnkN83wK3AB0BOMAfXiEDGPQ/4UNO0wwCapnWGsQcybg2IUEopIBxPAHIGd5h1BqRp31SPoyGdcZ8MlMSc4JKYE1xdMuaAxB065+eTuBNcXTHuSMzpnCTmBJfEnODqknEnGDGnsyR4UoEjtb7PrP5Zc7cJtuaO6Vo8GbmO1uS4lVKpwBzgpSCOqymB/L7TgWil1H+VUpuVUlcFbXQNC2TczwODgGPANuAPmqa5gzO8FuuM+2SgJOYEl8Sc4DpZYw50zv0yUBJ3gkviTvBIzOmcJOYEl8Sc4DpZ406r90lDmw6n5ZSfn9Xt3x7INsEW8JiUUtPxBKDJ7TqiwAQy7meAP2ua5vIkPTuFQMZtAMYAZwIhwAal1EZN0/a29+AaEci4zwF+Bs4A+gGrlFJrNU0raeextUZn3CcDJTEnuCTmBNfJGnOgc+6XgZK4E1wSd4JHYk7nJDEnuCTmBNfJGndavU92lgRPJtCj1vdpeDJtzd0m2AIak1JqOPAKcJ6maflBGltjAhn3WGBpdfCJA2YqpZyapi0Pygj9C/TvJE/TtHKgXCn1DTAC6MgAFMi4rwYe0zRNA/YppQ4CA4HvgzPEFumM+2SgJOYEl8Sc4DpZYw50zv0yUBJ3gkviTvBIzOmcJOYEl8Sc4DpZ407r90mtgwskeX7fGIADQB9OFEkaUmeb8/EtOPR9Fxl3T2AfMLGjx9uccdfZ/nU6RxGwQH7fg4CvqrcNBbYDQ7vAuF8EFlZ/nQgcBeI6we+8Nw0XAet0+2Qb/z/pdJ9PYk7nG7fEnHYZv8SdTvT5JO50vnF3trgjMadz/pOY0/nGXWd7iTntP+5OGXfaO+Z0ihk8mqY5lVK3AF/iqYj9qqZpO5RSN1Y//hKeSuMz8ezMFXgych0qwHE/AMQCL1Rna52apo3tqDFXjyuQcXc6gYxb07RdSqkvgK2AG3hF0zS/beiCJcDf98PA60qpbXh26D9rmpbXYYMGlFLvANOAOKVUJrAAMELn3ScDJTEnuCTmBFdXjTkgcYdO+Pkk7gRXV4w7EnM6J4k5wSUxJ7i6atwJRsxR1ZkiIYQQQgghhBBCCNFFdZYuWkIIIYQQQgghhBCihSTBI4QQQgghhBBCCNHFSYJHCCGEEEIIIYQQoouTBI8QQgghhBBCCCFEFycJHiGEEEIIIYQQQoguThI8QgghhBBCCCGEEF2cJHiEEEIIIYQQQgghurj/B28bN37AowW+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1152x216 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# Make predictions\n",
    "model.eval()\n",
    "with torch.no_grad(), gpytorch.settings.fast_pred_var():\n",
    "    test_x = torch.linspace(0, 1, 51).unsqueeze(-1)\n",
    "    mean, var = model.predict(test_x)\n",
    "    lower = mean - 2 * var.sqrt()\n",
    "    upper = mean + 2 * var.sqrt()\n",
    "\n",
    "# Plot results\n",
    "fig, axs = plt.subplots(1, num_tasks, figsize=(4 * num_tasks, 3))\n",
    "for task, ax in enumerate(axs):\n",
    "    ax.plot(train_x.squeeze(-1).detach().numpy(), train_y[:, task].detach().numpy(), 'k*')\n",
    "    ax.plot(test_x.squeeze(-1).numpy(), mean[:, task].numpy(), 'b')\n",
    "    ax.fill_between(test_x.squeeze(-1).numpy(), lower[:, task].numpy(), upper[:, task].numpy(), alpha=0.5)\n",
    "    ax.set_ylim([-3, 3])\n",
    "    ax.legend(['Observed Data', 'Mean', 'Confidence'])\n",
    "    ax.set_title(f'Task {task + 1}')\n",
    "fig.tight_layout()\n",
    "None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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   "codemirror_mode": {
    "name": "ipython",
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   "file_extension": ".py",
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   "pygments_lexer": "ipython3",
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